Patent Publication Number: US-2021185942-A1

Title: Managing stages of growth of a crop with micro-precision via an agricultural treatment delivery system

Description:
FIELD 
     Various embodiments relate generally to computer software and systems, computer vision and automation to autonomously identify and deliver for application a specific treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, wired and wireless network communications, and robotics and mobility technologies to navigate a delivery system, as well as vehicles including associated mechanical, electrical and electronic hardware, among objects in a geographic boundary to apply any number of treatments to objects, and, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. 
     BACKGROUND 
     Global human population growth is expanding at a rate projected to reach 10 billion or more persons within the next 40 years, which, in turn, will concomitantly increase demands on producers of food. To support such population growth, food producers, including farmers, need to generate collectively an amount of food that is equivalent to an amount that the entire human race, from the beginning of time, has consumed up to that point in time. Many obstacles and impediments, however, likely need to be overcome or resolved to feed future generations in a sustainable manner. For example, changes to the Earth&#39;s climate and unpredictable weather patterns negatively impact maintenance or enhancements in crop yields. Furthermore, limited or shrinking amounts of arable land on which to farm reduces opportunities to grow crops or dedicate land for other food production purposes. 
     Increased scarcity and costs of resources to produce food affects most farmers in less developed countries, as well as smaller farmers in developed countries. For example, the costs of crops sold (“cost of goods sold,” or “COGS”) are likely to increase beyond a range 60% to 70% of revenue. Costs of producing food, such as crops, may include costs due to labor, chemicals (e.g., fertilizer, pesticides, etc.), packaging, provenance tracking, and capital equipment (e.g., tractors, combines, and other farm implements), among other activities or resources. Labor costs are expected to rise as the demand for agricultural labor increases while fewer persons enter the agricultural workforce. Some agricultural workers are relocating to urban areas in numbers that increase scarcity of labor, thereby causing an average age of an agricultural worker to rise. Equipment costs, including tractors and sprayers, as well as other farm implements (e.g., combines, plows, spreaders, planters, etc.) may require relatively large expenses to purchase or lease, as well as to maintain, fuel, and operate. 
     Costs relating to chemical inputs are likely to rise, too. For instance, health-related and environmental concerns may limit amounts and/or types of chemicals, such as certain pesticides, that can be used to produce vegetables, fruits, and other agricultural products. Also, while some advances in chemistries may be beneficial, these advanced chemistries may be unaffordable for most applications by smaller farms, or farms in underdeveloped countries, thereby possibly depriving farmers of optimal means to produce food. Further, applications of some chemistries, such as herbicides, pesticides, and fertilizers, on agricultural crops require sprayers to disperse chemicals in very small liquid droplets (e.g., using boom sprayers, mist sprayers, etc.). Spray nozzles generally have orifices or apertures oriented in a line substantially perpendicular to and facing the ground at a distance above the crops (relative to the soil), with the apertures designed to form overlapping flat fan or cone-shaped patterns of spray. Such conventional approaches to applying chemistries, however, usually results in amounts of spray falling upon non-intended targets, such as on the soil, which is wasteful. 
     The above-described costs likely contribute to increases in food prices and farm closures, and such costs may further hinder advances to improve crop yields to meet sufficiently the projected increases in human population. While functional, a few approaches to improve crop yields have been developed, and typically have a number of drawbacks. In some traditional approaches, information to assist crop development relies on multi-spectral imagery from satellites, aircraft, and/or drones. Multi-spectral imagery combined with location information, such as provided by Global Positioning Systems (“GPS”), enables coarse analysis of portions of a farm to determine soil characteristics, fertilizer deficiencies, topological variations, drainage issues, vegetation levels (e.g., chlorophyll content, absorption, reflection, etc.), and the like. Thus, multi-spectral may be used to identify various spectral, spatial, and temporal features with which to evaluate the status of a group of crops as well as changes over time. There are various drawbacks to rely on this approach. For example, data based on multi-spectral imagery are generally limited to coarse resolutions related to crop-related management. That is, multi-spectral imagery generally provides information related to certain areas or region including multiple rows of crops or specific acreage portions. Further, multi-spectral imagery is not well-suited for monitoring or analyzing botanical items granularly over multiple seasons. In particular, such imagery may be limited to detectable foliage, for example, midway through a crop season. Otherwise, multi-spectral imagery may be limited, at least in some cases, to a set of abiotic factors, such as the environmental factors in which a crop is grown, and, thus, may be insufficient to identify a specific prescriptive action (e.g., applying fertilizer, a herbicide, etc.) for one or more individual plants that may not be detectable using multi-spectral imagery techniques. As such, multi-spectral imagery may not be well-suited to analyze biotic factors, among other things. 
     In another traditional approach, known computer vision techniques have been applied to monitor agricultural issues at plant-level (e.g., as a whole plant, imaged from a top view generally). While functional, there are a number of drawbacks to such an approach. For instance, some computer-based analyses have been adapted to perform agricultural assessments with reliance on incumbent or legacy machinery and hardware, such as conventional tractors. The conventional tractors and other known implements are not well-suited to integrate with recent autonomous technologies to sufficiently navigate among crops to perform functions or tasks less coarsely, or to identify and perform less coarse tasks or treatments. For example, some conventional applications may vary rates of dispensing fertilizer based on specific prescriptive maps that rely on resolutions provided by GPS and multi-spectral imagery (e.g., satellite imagery). Hence, some conventional rates of applying fertilizer are generally at coarse resolutions in terms of square meters (i.e., over multiple plants). 
     In some traditional approaches, known computer vision techniques are typically implemented to identify whether vegetation is either an individual crop or a non-crop vegetation (i.e., a weed). Further, these traditional approaches spray a chemical to generally treat a plant holistically, such as applying an herbicide to a weed or fertilizer to a crop. However, there is a variety of drawbacks to these traditional approaches. These approaches are typically directed to annual, row-based crops, such as lettuce, cotton, soybeans, cabbage, or other annual vegetation, which generally grow shorter than other vegetation. Row-based crops, also known as “row crops,” typically are crops tilled and harvested in row sizes relative to agricultural machinery, whereby row crops naturally are rotated annually to replace entire vegetative entities (e.g., removal of corn stalks, etc.). Also, row-based crops are typically planted in row widths of, for example, 15, 20, or 30 inch row widths, with conventional aims to drive row widths narrower to reduce weed competition and increase shading of the soil, among other things. 
     In some traditional approaches, known computer vision techniques applied to row crops rely on capturing imagery of vegetation at a distance above the ground and oriented generally parallel to a direction of gravity (e.g., top-down). As such, the background of imagery is typically soil, which may simplify processes of detecting vegetation relative to non-vegetation (e.g., the ground). Further, images captured by a camera may have relatively minimal a depth of view, such as a distance from the soil (e.g., as a farthest element) to the top of an individual crop, which may be a row crop. Row crops have relatively shorter depth of view than other vegetation, including trees or the like, or other configurations. Known computer vision techniques have also been used, in some cases, to apply a fertilizer responsive to identifying an individual plant. In these cases, fertilizer has been applied as a liquid, whereby the liquid is typically applied using streams or trickles of liquid fertilizer. Further, the application of liquid fertilizer generally relies on a gravitational force to direct a stream of fertilizer to the individual crop. 
     Thus, what is needed is a solution for facilitating application of a treatment to an identified agricultural object, without the limitations of conventional techniques. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments or examples (“examples”) of the invention are disclosed in the following detailed description and the accompanying drawings: 
         FIG. 1A  is a diagram depicting an example of an agricultural treatment delivery system, according to some embodiments; 
         FIG. 1B  is a diagram depicting an example of an emitter configured to apply a treatment, according to some examples; 
         FIG. 2A  is a diagram depicting examples of sensors and components of an agricultural treatment delivery vehicle, according to some examples; 
         FIG. 2B  depicts generation of indexed agricultural object data, according to some embodiments; 
         FIG. 3  is an example of a flow diagram to control agricultural treatment delivery system autonomously, according to some embodiments; 
         FIG. 4  is a functional block diagram depicting a system including a precision agricultural management platform communicatively coupled via a communication layer to an agricultural treatment delivery vehicle, according to some examples; 
         FIG. 5  is a diagram depicting another example of an agricultural treatment delivery system, according some examples; 
         FIG. 6  is an example of a flow diagram to align an emitter to a target autonomously, according to some embodiments; 
         FIGS. 7A and 7B  depict examples of data generated to identify, track, and perform an action for one or more agricultural objects in an agricultural environment, according to some examples; 
         FIG. 7C  is a diagram depicting parameters with which to determine activation of an emitter to apply a treatment, according to some examples; 
         FIG. 8  is a diagram depicting a perspective view of an agricultural projectile delivery vehicle configured to propel agricultural projectiles, according to some examples; 
         FIG. 9  is a diagram depicting an example of trajectory configurations to intercept targets autonomously using an agricultural projectile delivery vehicle, according to some examples; 
         FIG. 10  is a diagram depicting examples of different emitter configurations of agricultural projectile delivery systems, according to some examples; 
         FIG. 11  is a diagram depicting yet another example of an emitter configuration of an agricultural projectile delivery system, according to some examples; 
         FIGS. 12 and 13  are diagrams depicting examples of a trajectory processor configured to activate emitters, according to some examples; 
         FIG. 14  is a diagram depicting an example of components of an agricultural projectile delivery system that may constitute a portion of an emitter propulsion subsystem, according to some examples; 
         FIG. 15  is a diagram depicting an example of an arrangement of emitters oriented in one or more directions in space, according to some examples; 
         FIG. 16  is a diagram depicting an example of another arrangement of emitters configured to be oriented in one or more directions in space, according to some examples; 
         FIG. 17  is a diagram depicting one or more examples of calibrating one or more emitters of an agricultural projectile delivery system, according to some examples; 
         FIG. 18  is a diagram depicting another one or more examples of calibrating one or more emitters of an agricultural projectile delivery system, according to some examples; 
         FIG. 19  is an example of a flow diagram to calibrate one or more emitters, according to some embodiments; 
         FIGS. 20 and 21  are diagrams depicting an example of calibrating trajectories of agricultural projectiles in-situ, according to some examples; 
         FIG. 22  is a diagram depicting deviations from one or more optical sights to another one or more optical sights, according to some examples; 
         FIG. 23  is a diagram depicting an agricultural projectile delivery system configured to implement one or more payload sources to provide multiple treatments to one or more agricultural objects, according to some examples; 
         FIG. 24  is an example of a flow diagram to implement one or more subsets of emitters to deliver multiple treatments to multiple subsets of agricultural objects, according to some embodiments; 
         FIG. 25  is an example of a flow diagram to implement one or more cartridges as payload sources to deliver multiple treatments to multiple subsets of agricultural objects, according to some embodiments; 
         FIGS. 26 to 31  are diagrams depicting components of an agricultural treatment delivery vehicle configured to sense, monitor, analyze, and treat one or more agricultural objects of a fruit tree through one or more stages of growth, according to some examples; 
         FIG. 32  is a diagram depicting an example of a flow to manage stages of growth of a crop, according to some examples; 
         FIG. 33  is a diagram depicting an agricultural projectile delivery vehicle implementing an obscurant emitter, according to some examples; 
         FIG. 34  is a diagram depicting an example of a flow to facilitate imaging a crop in an environment with backlight, according to some examples; 
         FIG. 35  is a diagram depicting a pixel projectile delivery system configured to replicate an image on a surface using pixel projectiles, according to some embodiments; 
         FIG. 36  is a diagram depicting an example of a pixel projectile delivery system, according to some examples; 
         FIG. 37  is a diagram depicting an example of a flow to implement a pixel projectile delivery system, according to some examples; and 
         FIG. 38  illustrates examples of various computing platforms configured to provide various functionalities to components of an autonomous agricultural treatment delivery vehicle and fleet service, according to various embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments or examples may be implemented in numerous ways, including as a system, a process, an apparatus, a user interface, or a series of program instructions on a computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in an arbitrary order, unless otherwise provided in the claims. 
     A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples, but is not limited to any particular example. The scope is limited only by the claims, and numerous alternatives, modifications, and equivalents thereof. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description. 
       FIG. 1A  is a diagram depicting an example of an agricultural treatment delivery system, according to some embodiments. Diagram  100  depicts an agricultural treatment delivery system configured to identify an agricultural object to apply an agricultural treatment. Examples of an agricultural treatment delivery system includes agricultural treatment delivery system  111   a  and agricultural treatment delivery system  111   b , whereby agricultural treatment delivery systems  111   a  and  111   b  may be configured to deliver same or different treatments into an environment in which agricultural objects may be present. Agricultural treatment delivery system  111   a  may include one or more emitters, such as emitter  112   c . Emitter  112   c  may be configured emit a treatment  112   b , for example, via a trajectory  112   d  in any direction to intercept a target (“T”)  112   a  as vehicle  110  traverses path portions  119  at a velocity, v. In some cases, vehicle  110  may be in a static position. A direction of trajectory  112   d  may be within a two- or three-dimensional space described relative to an XYZ coordinate system or the like. Examples of target  112   a  may include a bud, blossom, or any other botanical or agricultural object likely to be sensed in an environment within a geographic boundary  120 , which may include at least a portion of a farm, an orchard, or the like. 
     Agricultural treatment delivery system  111   a  may be disposed in a vehicle, such as vehicle  110 , to facilitate mobility to any number of targets  112   a  within a geographic boundary  120  to apply a corresponding treatment  112   b . In some examples, vehicle  110  may include functionalities and/or structures of any motorized vehicle, including those powered by electric motors or internal combustion engines. For example, vehicle  110  may include functionalities and/or structures of a truck, such as a pick-up truck (or any other truck), an all-terrain vehicle (“ATV”), a utility task vehicle (“UTV”), or any multipurpose off-highway vehicle, including any agricultural vehicle, including tractors or the like. Also, agricultural treatment delivery systems  111   a  and  112   b , as well as other agricultural treatment delivery systems (not shown), may be implemented in a trailer (or other mobile platform) that may be powered or pulled separately by a vehicle, which may navigate path portions  119  manually or autonomously. As shown, vehicle  110  may include a manual controller or control module (“cab”)  115 , which may accommodate a human driver and include any mechanical control system, such as a steering wheel and associated linkages to steerable wheels, as well as manually controlled braking and accelerator subsystems, among other subsystems. 
     In some examples, vehicle  110  may include a mobility platform  114  that may provide logic (e.g., software or hardware, or both), and functionality and/or structure (e.g., electrical, mechanical, chemical, etc.) to enable vehicle  110  to navigate autonomously over one or more paths  119 , based on, for example, one or more treatments to be applied to one or more agricultural objects. Any of agricultural treatment delivery systems  111   a  or  111   b  may be configured to detect, identify, and treat agricultural objects autonomously (e.g., without manual intervention) independent of whether vehicle  110  is configured to navigate and traverse path portions  119  either manually or autonomously. 
     In the example shown, agricultural treatment delivery system  111  may be configured to traverse path portions  119  adjacent to agricultural objects, such as trees, disposed in arrangements  122   a ,  122   b ,  122   c ,  122   d , and  122   n , or any other agricultural objects associated therewith. In some cases, arrangements  122   a ,  122   b ,  122   c ,  122   d , and  122   n  may be any trellis-based structure, such as any espalier-supported pattern or any trellis configuration (e.g., substantially perpendicular configurations, an example of which is shown in diagram  100 , or “V-shaped” trellises, or any other structure). Note that agricultural treatment delivery system  111  need not be limited to trellis-based structures, but rather may be used with any plant or vegetative structure. 
     Any of agricultural treatment delivery systems  111   a  or  111   b  may be configured to operate, for example, in a sensor mode during which a sensor platform  113  may be configured to receive, generate, and/or derive sensor data from any number of sensors as vehicle  110  traverses various path portions  119 . For example, sensor platform  113  may include one or more image capture devices to identify and/or characterize an agricultural object, thereby generating. Examples of image capture devices include cameras (e.g., at any spectrum, including infrared), Lidar sensors, and the like. Image-based sensor data may include any include any data associated with an agricultural object, such as images and predicted images, that may describe, identify, or characterize physical attributes. Sensor platform  113  may also include one or more location or position sensors, such as one or more global positioning system (“GPS”) sensors and one or more inertial measurement units (“IMU”), as well as one or more radar devices, one or more sonar devices, one or more ultrasonic sensors, one or more gyroscopes, one or more accelerometers, one or more odometry sensors (e.g., wheel encoder or direction sensors, wheel speed sensors, etc.), and the like. Position-based sensors may provide any data configured to determine locations of an agricultural object relative to a reference coordinate system, to vehicle  110 , to emitter  112   c , or to any other object based, for example, GPS data, inertial measurement data, and odometry data, among data generated by other position and/or location-related sensors. 
     Note that any sensor or subset of sensors in sensor platform  113  may be configured to provide sensor data for any purpose. For example, any image capture device may be configured to detect a visual fiducial marker or any other optically-configured item (e.g., a QS code, barcode, or the like) that may convey information, such as position or location information, or other information. As agricultural treatment delivery system  111  and/or sensory platform  113  traverses path portions  119 , image capture devices may detect fiducial markers, reflective surfaces, or the like, so that logic within sensory platform  113  or vehicle  110  (e.g., one or more processors and one or more applications including executable instructions) may be configured to detect or confirm a position of vehicle  110  or emitter  112   c , or both, as a position within geographic boundary  120  or relative to an agricultural object. 
     In some implementations, one or more sensors in sensor platform  113  may be distributed among any portion of vehicle in any combination. For example, sensors in sensor platform  113  may be disposed in any of agricultural treatment delivery systems  111   a  or  111   b . As such, any of agricultural treatment delivery systems  111   a  or  111   b  may each be configured to localize or determine a position of emitter  112   c  relative to an agricultural object independently. That is, agricultural treatment delivery systems  111   a  or  111   b  may include sensors and logic to determine the position of an emitter  112   c  in association with, or relative to, an agricultural object, and may be configured to identify an agricultural object autonomously, orient or otherwise target that agricultural object for action autonomously, and/or perform an action or apply a treatment autonomously regardless of whether vehicle  110  is navigating manually or autonomously. 
     One or more of agricultural treatment delivery systems  111   a  and  111   b  may be implemented as a modular structure that may be loaded into a bed of a pickup truck or an ATV/UTV, or any other vehicle that may be configured to manually navigate agricultural treatment delivery system  111  along path portions  119  to sense an agricultural object and to perform an action therewith. In some implementations, one or more sensors in sensor platform  113  may be distributed among any portion of vehicle, for example, including mobility platform  114 , to facilitate autonomous navigation, agricultural object identification, and perform an action (e.g., apply a treatment). Hence, agricultural treatment delivery system  111  may implement sensors or sensor data (e.g., individually or collectively), or may share or use sensors and sensor data used in association with mobility platform  114  to facilitate autonomous navigation of vehicle  110 . In one example, sensor platform  113  may be disposed in mobility platform  114 , or in or among any other portion. Mobility platform  114  may include hardware or software, or any combination thereof, to enable vehicle  110  to operate autonomously. 
     Sensor platform  113  may be configured to sense, detect, analyze, store, and/or communicate data associated with one or more agricultural objects. For example, sensor platform  113  may be configured to at least detect or sense a subset  123   b  of one or more agricultural objects associated with tree  121   a  positioned adjacent path portion  119   a . Sensor platform  113  also may be configured to detect subset  123   b  of agricultural objects as, for example, a limb, a branch, or any portion of agricultural object (“tree”)  121   a , and may further detect other sub-classes of agricultural objects of subset  123   b . Sub-classes of agricultural objects of subset  123   b  may include buds, such as growth buds  125  (e.g., a bud from which a leaf or shoot may develop) and fruit buds  124  and  126 , each of which may be an agricultural object. Branch  123   a  may also include a limb  127  as an agricultural object, and may include other agricultural objects, such as a spur (e.g., a shoot that may develop fruit), a water sprout (e.g., a young shoot growing within tree  121   a ), and the like. 
     In some embodiments, agricultural treatment delivery system  111  may be configured to communicate agricultural object data  197  via any communication media, such as wireless radio transmissions, to a precision agricultural management platform  101 . Precision agricultural management platform  101  may include hardware (e.g., processors, memory devices, etc.) or software (e.g., applications or other executable instructions to facilitate machine learning, deep learning, computer vision techniques, statistical computations, and other algorithms), or any combination thereof. Precision agricultural management platform  101  (or portions thereof) may reside at any geographic location, whether at or external to geographic location  120 . In one or more examples, logic associated with either precision agricultural management platform  101  or agricultural treatment delivery system  111 , or both, may be configured to implement or facilitate implementation of simultaneous localization and mapping (“SLAM”) to support autonomous navigation of vehicle  110 , as well as autonomous operation of agricultural treatment delivery systems  111   a  and  111   b . Hence, agricultural treatment delivery systems  111   a  and  111   b  may implement SLAM, or any other technique, to apply a treatment to an agricultural object autonomously. 
     Precision agricultural management platform  101  may be configured to, index and assign a uniquely identifier to each agricultural object in transmitted data  197  (e.g., as a function of a type of agricultural object, such as a blossom, a location of the agricultural object, etc.). Precision agricultural management platform  101  also may operate to store and manage each agricultural object (in agricultural object data  197 ) as indexed agricultural object data  102   a , whereby each data arrangement representing each indexed agricultural object may be accessed using an identifier. 
     In some cases, either precision agricultural management platform  101  or agricultural treatment delivery system  111 , or both, may be configured to implement computer vision and machine learning algorithms to construct and maintain a spatial semantic model (e.g., at resolutions of sub-centimeter, or less) and/or a time-series model of plant physiology and state-of-growth. Data representing any of these models may be disposed in data representing indexed agricultural object data  102   a . For example, agricultural treatment delivery system  111  may be configured to navigate path portions  119  at any time in autumn, winter, spring, and summer to monitor a status of tree  121   a  and associated subsets of agricultural objects. For example, sensor platform  113  and/or agricultural treatment delivery system  111  may capture sensor data associated with fruit bud  126 , which may develop over time through progressive stages of growth. At stage  130 , fruit bud  126  is shown in an “open cluster” stage with flower buds  131  being, for example, pink in color and prior to blossom. The open cluster stage is indicated at time, T, equivalent to t(OC), with “OC” referring to “open cluster.” At stage  132 , the open cluster may transition over time (and through other intermediate stages, which are not shown) into a “blossom” stage in which a first (e.g., King) blossom  133  opens. The blossom stage is indicated at time, T, equivalent to t(lst BL), with “BL” referring to “blossom.” At stage  133 , the blossom stage may transition over time (and through other intermediate stages, which are not shown) into a “fruit” stage in which a first blossom ripens into a fruit  135 . The fruit stage is indicated at time, T, equivalent to t(opt), with “opt” referring to an optimal time at which fruit  135  may be optimally ripened for harvest. 
     Continuing with the example of detecting various stages in the growth of a crop, sensor platform  113  may transmit sensor data as agricultural object data  197  to precision agricultural management platform  101 , which may analyze sensor data representing one of stages  130 ,  132 , and  134  to determine an action to be performed for a corresponding stage. For example, at stage  132 , logic in precision agricultural management platform  101  may be configured to identify at least one action to be performed in association with blossom  133 . An action may include applying a treatment to blossom  133 , such as causing emitter  112   c  to apply treatment  112   b  to blossom  133  as target  112   a . The treatment may include applying pollen to, for example, a stigma of blossom  133  to effect germination. In various examples, precision agricultural management platform  101  may be configured to generate and store policy data  102   b  in repository  102 , and be further configured to transmit policy and/or index data  195  to agricultural treatment delivery system  111   a.    
     Any of agricultural treatment delivery systems  111   a  or  111   b  may be configured to operate, for example, in an action mode during which a sensor platform  113  may be configured to receive policy data  195  associated with blossom  133 , which may be uniquely identifiable as an indexed agricultural object. Policy data may specify that blossom  133  is to be pollenated. Sensor platform  113  may be configured further to receive and/or generate sensor data from any number of sensors as vehicle  110  traverses various path portions  119 , the sensor data being configured to identify an image of blossom  133  as vehicle traverses path portion  119   a . When sensor platform  113  detects blossom  133 , agricultural treatment delivery system  111   a  may be configured to trigger emission of treatment  112   b  autonomously. Note that agricultural treatment delivery systems  111   a  or  111   b  each may operate in sensor and action modes, individually or simultaneously, and may operate in any number of modes. Each mode may be implemented individually or collectively with any other mode of operation. 
     In some examples, agricultural treatment delivery systems  111  and vehicle  110  may operate to provide “robotics-as-a-service,” and in particular, “robotics-as-an-agricultural-service” to enhance automation of crop load management and yield enhancement at least an agricultural object basis (or at finer resolution). For example, an apple crop may be monitored (e.g., during each pass of vehicle  110 ) and treated with micro-precision, such as on a per-cluster basis or a per-blossom basis. Vehicle  110  and other equivalent vehicles  199  may constitute a fleet of autonomous agricultural vehicles  110 , each of which may identify agricultural objects and apply corresponding treatments autonomously. In at least one example, vehicle  110  may be configured to traverse, for example, 50 acres (or more) at least two times each day to generate and monitor sensor data, as well as to apply various treatments. In some cases, a computing device  109  may be configured to receive sensor data (e.g., image data) and to transmit executable instructions to perform a remote operation (e.g., a teleoperation) under guidance of user  108 , who may be an agronomist or any other user including data analysts, engineers, farmers, and the like. Computing device  109  may provide remote operation to either navigate vehicle  110  or apply a treatment  112   b  via emitter  112   c , or both. 
       FIG. 1B  is a diagram depicting an example of an emitter configured to apply a treatment, according to some examples. Diagram  150  includes an emitter  152   c  that may be configured to apply treatments to agricultural objects, such as one or more portions of agricultural object depicted as a blossom. An agricultural treatment delivery system may be configured to apply units (e.g., distinct units) of treatment that may include, for example, packetized portions of a fertilizer, a thinning chemical, an herbicide, a pesticide, or any other applicable agricultural material or substance. As shown, emitter  152   c  may be configured to apply a treatment with micro-precision by emitting an agricultural projectile  152   b  to intercept a target  152   ab  at or within a target dimension  151 . For example, target dimension  151  may have a dimension (e.g., a diameter) of 1 centimeter (“cm”) or less. Hence, emitter  152   c  may deliver a treatment with a micro-precision of, for example, 1 cm or less, at any trajectory angle. 
     Agricultural projectile  152   b  may be configured as a liquid-based projectile propelled from emitter  152   c  for a programmable interval of time to form the projectile having, for example, an envelope  156   b , at least in one example. Emitter  152   c  may be configured to emit agricultural projectile  152   b  along a trajectory direction  155   a , which may be any direction in two or three-dimensional space. In at least one example, emitter  152   c  may be configured to propel agricultural projectile  152   b  in trajectory direction  155   a  with a force having a vertical component (“Fvc”) and a horizontal component (“Fhc”). As shown, the vertical component of the propulsion force may be in a direction opposite than the force of gravity (“Fg”). Note, however, any of vertical component (“Fvc”) and a horizontal component (“Fhc”) may be negligible or zero, at least in one implementation. Note, too, that horizontal component (“Fhc”) may have a magnitude sufficient to propel agricultural projectile  152   b  over a trajectory distance  154 . In treatments applied to, for example, trellis or orchard crops, trajectory distance  154  may be 3 meters or less. In treatments applied to, for example, row crops, trajectory distance  154  may be 1 meter or less. In at least one example, trajectory distance  154  may be any distance within a geographic boundary. 
     In at least one other example, emitter  152   c  may be configured to emit agricultural projectile  152   b  along trajectory direction  155   a  in an envelope  156   a  to intercept a target  152   aa  having a target dimension  153 . As an example, target dimension  153  may have a dimension (e.g., a diameter) equivalent to a size of an apple (or a size equivalent to any other agricultural object). Thus, emitter  152   c  may be configured to modify a rate of dispersal with which portions of agricultural projectile  152   b  disperses at or about a range of a target of a particular size. According to some examples, trajectory direction  155   a  may be coaxial with an optical ray extending, for example, from at least one of a first subset of pixels of an image capture device to at least one pixel of a second subset of pixels including an image of target  152   ab . Further, one or more of the first subset of pixels may be configured as an optical sight. Thus, when at least one pixel of the optical sight aligns with at least one pixel of the target image, an agricultural projectile may be propelled to intercept target  152   ab . In some examples, emitter  152   c  may include an aperture that is aligned coaxially with the optical ray. An example of emitter  152   c  includes a nozzle. According to at least one implementation, disposing and orienting emitter  152  coaxially to an optical ray facilitates, for example, two-dimensional targeting. Therefore, a range or distance of target  152   b  may be positioned anywhere, such as at points A, B, or C, along the optical ray and may be intercepted without calculating or confirming an actual or estimated distance or three-dimensional position, at least in some examples. 
     In some examples, a dosage or amount of treatment (e.g., fertilizer) may be applied at variable amounts by, for example, slowing a speed of vehicle and extending an interval during which agricultural projectile  152   b  is propelled or emitted. In at least one case, an amount of propulsion (e.g., a value of pressure of a propellant, such as a compressed gas) may be modified as a function of trajectory distance  154  or any other factor, such as an amount of wind. In some cases, multiple agricultural projectile  152   b  of the same or different amounts may be propelled to intercept a common target  152   a . Agricultural projectile  152   b  may include an inert liquid to increase viscosity, which may reduce a rate of dispersal, at least in some implementations. In some cases, emitter  152   c  can be configured to emit agricultural projectile  152   b  having a liquid configured to be emitted with a laminar flow characteristics (e.g., with minimal or negligible turbulent flow characteristics). According to other examples, an emitter need not align with an optical ray and may use multiple image devices to orient alignment of an emitter to a target independently of an optical ray associated with an in-line camera. 
       FIG. 2A  is a diagram depicting examples of sensors and components of an agricultural treatment delivery vehicle, according to some examples. Diagram  200  depicts expanded component view of an agricultural treatment delivery vehicle  210  that may provide a mechanical and electrical structure, such as structures implemented in pick-up tracks, flatbed trucks, ATVs, UTVs, tractors, and the like. Agricultural treatment delivery vehicle  210  may include a power plant, such as an internal combustion engine or an electric battery-powered motor. As shown, agricultural treatment delivery vehicle  210  may include a sensor platform  213  including any number and type of sensor, whereby any sensor may be located and oriented anywhere on agricultural treatment delivery vehicle  210 . For example, sensors depicted in diagram  200  may be implemented as sensor platform  213  (or a portion thereof). Sensors in  FIG. 2A  include one or more image capture sensors  236  (e.g., light capture devices or cameras of any type, including infrared camera to perform action at night or without sunlight), one or more radar devices  237 , one or more sonar devices  238  (or other like sensors, including ultrasonic sensors or acoustic-related sensors), and one or more Lidar devices  234 , among other sensor types and modalities (some of which may not be shown, such as inertial measurement units, or “IMUS,” global positioning system (“GPS”) sensors, temperature sensors, soil composition sensors, humidity sensors, barometric pressure sensors, light sensors, etc.). In some cases, sensor platform  213  may also include an airflow direction sensor  201  (e.g., wind direction) and/or an airflow speed sensor  202  (e.g., wind speed), where the direction and speed of air flow may be relative to a direction and velocity of agricultural treatment delivery vehicle  210 . 
     Agricultural treatment delivery vehicle  210  may include an agricultural treatment delivery system  211  including any number of emitters  212 , each of which may be oriented to propel an agricultural projectile at any direction from a corresponding emitter. In some examples, each emitter  212  may be oriented to propel an agricultural projectile via a trajectory that may be coaxial to an optical ray associated with a portion of a digitized image of an agricultural environment that includes one or more crops, or agriculture objects. 
     Further, agricultural treatment delivery vehicle  210  may include a motion estimator/localizer  219  configured to perform one or more positioning and localization functions. In at least one example, motion estimator/localizer  219  may be configured to determine a location of one or more component of agricultural treatment delivery vehicle  210  relative to a reference coordinate system that may facilitate identifying a location at specific coordinates (i.e., within a geometric boundary, such as an orchard or farm). For example, motion estimator/localizer  219  may compute a position of agricultural treatment delivery vehicle  210  relative to a point associated with vehicle  210  (e.g., a point coincident with a center of mass, a centroid, or any other point of vehicle  210 ). As another example, a position of agricultural treatment delivery system  211  or any emitter  212  may be determined relative to a reference coordinate system, or relative to any other reference point, such as relative to a position of agricultural treatment delivery vehicle  210 . In yet another example, a position of an agricultural object may be determined using sensors in platform  213  and motion estimator/localizer  219  to calculate, for example, a relative position of an agricultural object relative to a position of emitter  212  to facilitate identification of an indexed agricultural object (e.g., using image sensors data) and to enhance accuracy and precision of delivering an agricultural project to a target. According to some embodiments, data describing a position may include one or more of an x-coordinate, a y-coordinate, a z-coordinate (or any coordinate of any coordinate system), a yaw value, a roll value, a pitch value (e.g., an angle value), a rate (e.g., velocity), altitude, and the like. 
     In some examples, motion estimator/localizer  219  may be configured to receive sensor data from one or more sources, such as GPS data, wheel data (e.g., odometry data, such as wheel-related data including steering angles, angular velocity, etc.), IMU data, Lidar data, camera data, radar data, and the like, as well as reference data (e.g., 2D map data and route data). Motion estimator/localizer  219  may integrate (e.g., fuse the sensor data) and analyze the data by comparing sensor data to map data to determine a local position of agricultural treatment delivery vehicle  210  or emitter  212  relative to an agricultural object as a target, or relative to a waypoint (e.g., a fiducial marker affixed adjacent a path). According to some examples, motion estimator/localizer  219  may generate or update position data in real-time or near real-time. 
     Agricultural treatment delivery vehicle  210  may include a mobility controller  214  configured to perform one or more operations to facilitate autonomous navigation of agricultural treatment delivery vehicle  210 . For example, mobility controller  214  may include hardware, software, or any combination thereof, to implement a perception engine (not shown) to facilitate classification of objects in accordance with a type of classification, which may be associated with semantic information, including a label. A perception engine may classify objects for purposes of navigation (e.g., identifying a path, other vehicles, trellis structures, etc.), as well as for purposes of identifying agricultural objects to which a treatment may be applied. For example, a perception engine may classify an agricultural object as a bud, a blossom, a branch, a spur, a tree, a cluster, a fruit, etc. Mobility controller  214  may include hardware, software, or any combination thereof, to implement a planner (not shown) to facilitate generation and evaluation of a subset of vehicle trajectories based on at least a location of agricultural treatment delivery vehicle  210  against relative locations of external dynamic and static objects. The planner may select an optimal trajectory based on a variety of criteria over which to direct agricultural treatment delivery vehicle  210  in way that provides for collision-free travel or to optimize delivery of an agricultural projectile to a target. In some examples, a planner may be configured to calculate the trajectories as probabilistically-determined trajectories. Mobility controller  214  may include hardware, software, or any combination thereof, to implement a motion controller (not shown) to facilitate conversion any of the commands (e.g., generated by the planner), such as a steering command, a throttle or propulsion command, and a braking command, into control signals (e.g., for application to actuators, linkages, or other mechanical interfaces  217 ) to implement changes in steering or wheel angles and/or velocity autonomously. 
     In some examples, agricultural treatment delivery system  211 , sensor platform  213  (including any sensor), and motion estimator/localizer  219  may be implemented as a modular agricultural treatment delivery system  221 , which can be configured to autonomously identify agricultural objects and apply a treatment to each agricultural object in accordance with a policy (e.g., application of a certain treatment responsive to stage of growth, and environmental condition, biotic data, abiotic data, etc.). Therefore, modular agricultural treatment delivery system  221  may be disposed in a truck, ATV, tractor, etc., any of which may be navigated manually (e.g., by a human driver) using manual control order  215 . In some example, agricultural treatment delivery system  211  may have logic, similar or equivalent to that in mobility controller  214 . For instance, agricultural treatment delivery system  211  may be configured to implement one or more of a perception engine to detect and classify agricultural objects, a planner to determine actions (e.g., one or more trajectories over which to propel an agricultural projectile), and a motion controller to control, for example, position or orientation of emitter  212 . In other examples, agricultural treatment delivery system  211 , sensor platform  213  (including any sensor), and motion estimator/localizer  219  each may integrated into a modular agricultural treatment delivery system  221 , which, in turn, may be integrated into agricultural treatment delivery vehicle  210 , along with mobility controller  214 , to facilitate autonomous navigation of vehicle  210  and autonomous operation of agricultural treatment delivery system  211 . 
     While agricultural treatment delivery vehicle  210  is described for applications in agriculture, delivery vehicle  210  need not be so limiting and may be implemented in any other type of vehicle, whether on land, in air, or at sea. Further, any agricultural projectile described herein, need not be limited to liquid-based projectiles, and may include solid and gas-based emissions or projectiles. Moreover, agricultural treatment delivery vehicle  210  need not be limited to agriculture, but may be adapted for any of a number of non-agricultural applications. Also, agricultural treatment delivery vehicle  210  may be configured to communicate with a fleet  299  of equivalent delivery vehicles to coordinate performance of one or more policies for any geographic boundary. 
       FIG. 2B  depicts generation of indexed agricultural object data, according to some embodiments. Diagram  250  depicts an agricultural treatment delivery system  211 , which, in turn, may optionally include a sensor platform  213  and a motion estimator/localizer  219 , according to some examples. While sensor platform  213  is shown to include motion estimator/localizer  219 , each may be separate or distributed over any number of structures (as well as constituent components thereof). Diagram  250  also depicts a precision agricultural management platform  201  configured to receive agricultural object data  251  from agricultural treatment delivery system  211 , and further configured to generate indexed agricultural object data  252   a , which may be stored in a data repository  252 . Note that elements depicted in diagram  250  of  FIG. 2B  may include structures and/or functions as similarly-named elements described in connection to one or more other drawings. 
     Agricultural object data  251  may include data associated with a non-indexed agricultural object, an updated agricultural object, or any other information about an agricultural object. In some instances, a non-indexed agricultural object may be an agricultural object detected at sensor platform  213 , and may yet to be identified in, or indexed into, a database of indexed agricultural object data  252   a . Precision agricultural management platform  201  may be configured to identify agricultural object data  251  as “non-indexed,” and may activate executable instructions to invoke indexing logic  253  to generate indexed agricultural object data  252   a  based on agricultural object data  251 , whereby indexed identifier data  254  (e.g., a unique identifier) may be associated with agricultural object data  251 . Also, agricultural object data  251  may include data associated with an updated agricultural object, such as when an agricultural object identified as being in a bud state at one point in time transitions to a blossom state (or any other intermediate states) at another point in time. In this case, agricultural object data  251  may also include image data (e.g., data representing a blossom) as well as any other sensor-based or derived data associated therewith, including an identifier (e.g., previously determined). 
     In various examples, precision agricultural management platform  201  may be configured to determine for agricultural object data  251  any other associated data provided or derived by, for example, sensors in sensor platform  213  and motion estimator/localizer  219 . For example, precision agricultural management platform  201  may be configured to generate one or more of location data  255 , botanical object data  260  (e.g., crop-centric data), biotic object data  272 , abiotic object data  274 , predicted data  282 , action data  290 , as well as any other data associated with agricultural object data  251 , including agricultural object characteristics, attributes, anomalies, associated activities, environmental factors, ecosystem-related items and issues, conditions, etc. Any one or more of location data  255 , botanical object data  260  (e.g., crop-centric data), biotic object data  272 , abiotic object data  274 , predicted data  282 , and action data  290  may be included or omitted, in any combination. 
     Precision agricultural management platform  201  may be configured to identify a location (e.g., a spatial location relative to a two-dimensional or three-dimensional coordinate system) of an agricultural object associated with agricultural object data  251 , the location being represented by geographic location data  255   a . In some cases, geographic location data  255   a  may include a geographic coordinate relative to a geographic boundary, such as a boundary of an orchard or farm. Geographic location data  255   a  may include GPS data representing a geographic location, or any other location-related data (e.g., derived from position-related data associated with a sensor, vehicle, or agricultural treatment delivery system). Location data  255  may also include positioning data  255   b  that may include one or more subsets of data that may be used to determine or approximate a spatial location of the agricultural object associated with agricultural object data  251 . For example, position data for one or more optical markers (e.g., reflective tape, visual fiduciary markers, etc.) may be included in positioning data  255   b  to locate or validate a spatial location for agricultural object data  251 . 
     Botanical object data  260  may include any data associated with a plant, such as a crop (e.g., a specifically cultivated plant). For example, botanical object data  260  may include growth bud-related data  260 , fruit bud-related data set  262 , limb data  264 , and trunk/stem data  266 , and include other sub-classification of agricultural objects. Growth bud-related data  260  may include status data  261   a  that may identify a bud as being in a “bud state” at one point in time, which may be determined (at another point in time) to be in another state when the bud develops into either one or more leaves or a shoot. Physical data  261   b  may describe any attribute or characteristic of an agricultural object originating as a bud and that develops into one or more leaves or a shoot. For example, physical data  261   b  may include a shape, color, orientation, anomaly, or the like, including image data, or any characteristic that may be associated with a leaf as an agricultural object. 
     Fruit bud-related data set  262  may include status data  262   a  that may identify a fruit bud as being in a “fruit bud state” at one point in time, which may be determined (at another point in time) to be in another state when the bud develops into, for example, one or more blossoms as well as one or more fruit, such as one or more apples. For example, status data  262   a  may include a subset of data  264   a  to  264   k  to describe a status or a state of growth associated with a fruit bud. The following description of sets of data  264   a  to  264   k  are illustrative regarding stages of growth of apples, and is not intended to be limiting and can be modified for any fruit crop, vegetable crop, or any plant-related stages of growth, including ornamental plants, such as flowers (e.g., roses), and the like. 
     Dormant data  264   a  may include data associated with an identified dormant fruit bud, including image data acquired or otherwise sensed at one or more points in time as physical data  262   b . Silver tip data  264   b  may include data associated with a stage of growth relative to a fruit bud transitioning to a “silver tip” stage of growth, including one or more images thereof as physical data  262   b . In this stage, image data depicting a fruit bud may include digitized images of scales that may be separated at the tip of the bud, thereby exposing light gray or silver tissue. Green tip data  264   c  may include data associated with a stage of growth relative to a fruit bud transitioning to a “green tip” stage of growth, including one or more images thereof as physical data  262   b . In this stage, a fruit bud may have developed to include image data depicting a broken tip at which green tissue may be visible. Half-inch green data  264   d  may include data associated with a stage of growth relative to a fruit bud transitioning to a “half-inch” stage of growth, including one or more images thereof as physical data  262   b . At this stage, a fruit bud may have developed to include image data depicting a broken tip at which approximately one-half inch of green tissue may be detectable in an image. Tight cluster data  264   e  may include data associated with a stage of growth relative to a fruit bud transitioning to a “tight cluster” stage of growth, including one or more images thereof as physical data  262   b . At this stage, a fruit bud may have developed to include image data depicting a subset of blossom buds at various levels of visibility that may be detectable in an image, the blossom buds being tightly grouped. 
     Pink/pre-blossom data  264   f  may include data associated with a stage of growth relative to an initial fruit bud transitioning to a “pink” stage of growth (also known as “first pink,” “pre-pink,” or “full pink” stages) as well as (or up to) an “open cluster” stage, and one or more images thereof may be included in physical data  262   b . At this stage, image data may depict a subset of blossom buds at various levels of pink colors that may be detectable in an image prior to blossom. Blossom data  264   g  may include data associated with a stage of growth relative to an initial fruit bud that may transition to a “blossom” stage of growth (also known as aa “king bloom” or “king blossom” stage), and one or more images thereof may be included in physical data  262   b . At this stage, image data may depict a subset of pink blossom buds that include at least one blossom, such as a “king blossom,” in an image. 
     Multi-blossom data  264   h  may include data associated with a stage of growth relative to a “multi-blossom” stage of growth (also known as a “full bloom” stage). One or more images of an agricultural object in a “multi-blossom” stage of growth may be included in associated physical data  262   b . At this stage, image data may depict a number of blossoms (e.g., after pink blossom buds bloom). Petal fall data  264   j  may include data based on a transition from a “multi-blossom” stage to a “petal fall” stage of growth. One or more images of an agricultural object in a “petal fall” stage of growth may be included in associated physical data  262   b . At this stage, image data may depict a cluster of blossoms that have a threshold amount of lost petals (e.g., 60% to 80% fallen) that have detached from a central structure in an image. Fruit  264   k  may include data based on transitioning from a “petal fall” stage to a “fruit” stage of growth (also known as a “fruit set” stage). One or more images of an agricultural object in a “fruit” stage of growth may be included in associated physical data  262   b . At this stage, image data may depict a number of fruit (e.g., one or more apples relative to a cluster). 
     Limb data  264  may include status data  264   a  that may identify or classify a limb (e.g., a branch, a shoot, etc.) as being in a particular state at one point in time, which may be determined (at another point in time) to be in another state when the limb develops and grows. For example, limb data  264  can include data specifying a limb as being in a “non-supportive” state (i.e., the limb size and structure may be identified as being less likely to support growth of one or more apples to harvest). In this state, an agricultural treatment delivery system may be configured to apply a treatment, such as a growth hormone, to promote growth of the limb into, for example, a “supportive” state to facilitate growth of apples. Physical data  264   b  may describe any attribute or characteristic of an agricultural object identified as a limb. For example, physical data  264   b  may include a shape, size, color, orientation, anomaly, or the like, including image data, or any characteristic that may be associated with a limb as an agricultural object. Similarly, trunk/stem data  266  may include status data  266   a  that may identify or classify a truck or a stem (or a portion thereof) as being in a particular state at a point in time, whereas physical data  266   b  may describe any attribute or characteristic of an agricultural object identified as a trunk or stem (or a portion thereof). For example, physical data  266   b  may include a shape, size, dimensions, color, orientation, anomaly, or the like, including image data, or any characteristic that may be associated with a trunk or a stem as an agricultural object. 
     Biotic object data  272  may describe a living organism present in an ecosystem or a location in a geographic boundary. For example, biotic object data  272  may include status data  272   a  that may identify a type of bacteria, a type of fungi (e.g., apple scab fungus), a plant (e.g., a non-crop plant, such as a weed), and an animal (e.g., an insect, a rodent, a bird, etc.), and other biotic factors that may influence or affect growth and harvest of a crop. Status data  272   a  may also identify describe any attribute or characteristic of a biotic object. Positioning data  272   b  may include data describing whether a biotic object is positioned relative to, or independent from, another agricultural object (e.g., apple scab fungus may be identified as being positioned on an apple, which is another agricultural object). Positioning data  272   b  may be configured to locate or validate a spatial location of a biotic object as agricultural object data  251 . 
     Abiotic object data  274  may describe a non-living element (e.g., a condition, an environmental factor, a physical element, a chemical element, etc.) associated with an ecosystem or a location in a geographic boundary that may influence or affect growth and harvest of a crop. For example, abiotic object data  274  may include status data  274   a  that may identify soil constituents (e.g., pH levels, elements, and chemicals), a time of day when abiotic data is sensed, amounts, intensities, directions of light, types of light (e.g., visible, ultraviolet, and infrared light, etc.), temperature, humidity levels, atmospheric pressure levels, wind speeds and direction, amounts of water or precipitation, etc. Positioning data  272   b  may include data describing whether an abiotic object is associated with another agricultural object, or any other data configured to locate or validate a spatial location of an abiotic object, such as portion of soil that may be acidic. Further, agricultural object data  251  may include any other data  280 , which may include any other status data  280   a  and/or any other supplemental data  280   b.    
     Further to  FIG. 2B , precision agricultural management platform  201  may include analyzer logic  203  and a policy generator  205 . Analyzer logic  203  may be configured to implement computer vision algorithms and machine learning algorithms (or any other artificial intelligence-related techniques), as well as statistical techniques, to construct and maintain a spatial semantic model as well as a time-series model of physiology and/or physical characteristics of a crop (or any other agricultural object, such as a limb or branch) relative to a stage-of-growth. Analyzer logic  203  may be further configured to predict a next state or stage of growth and an associated timing (e.g., a point in time or a range of time) at which a transition may be predicted. Hence, analyzer logic  203  may be configured to generate predicted data  282  that may include predicted status data  282   a  to describe a predicted status of an agricultural object associated with indexed identifier data  254 . For example, a predicted status of a cluster of blossoms, as an agricultural object, may specify a predicted transition from a single opened blossom (e.g., a king blossom as an agricultural object) to one or more lateral blossoms opening (e.g., as corresponding agricultural objects), as well as a predicted range of time during which the predicted state transition may (likely) occur. Predicted data  282  may include predicted image data  282   b  that may be provided or transmitted to agricultural treatment delivery system  211  to facilitate detecting and identifying an agricultural object. Predicted image data  282   b  may be used to determine whether it may have transitioned from one state to the next (e.g., since previously being sensed or monitored). Further, predicted data  282  may include predicted action data  282   c  and any other predicted data  282   d , which may facilitate navigation and positioning of an emitter to apply a treatment optimally (e.g., emitting an agricultural projectile within a range of accuracy and/or a range of precision), for example, as a function of context (e.g., season, stage of growth, associated biotic and abiotic conditions, time of day, amount of sunlight, etc.). 
     In some examples, policy generator  205  may be configured to analyze a status (e.g., a current or last sensed status) of an agricultural object and a predicted status, and may be further configured to derive one or more actions as action data  290  as a policy. Action data  290  may be implemented as policy data that is configured to guide performance of one or more treatments to an agricultural object. For example, action data  290  associated with an agricultural object identified as a king blossom may include data representing a policy (e.g., a definition, rules, or executable instructions) to perform an action (e.g., pollinate a king blossom), whereas action data  290  associated with an agricultural object identified as a lateral blossom (e.g., in association with a cluster including a king blossom) may include policy data to perform a thinning action to terminate growth of the lateral blossom. Action data  290  may also include data representing prior actions performed as well as results based on those prior actions, as well as any other action-related data. 
       FIG. 3  is an example of a flow diagram to control agricultural treatment delivery system autonomously, according to some embodiments. At  302 , flow  300  begins to receive data configured to implement a policy to perform an action in association with an agricultural object. In some cases, data representing one or more actions to be performed relative to a subset of agricultural objects may be received at, for example, an agricultural treatment delivery system. Policy data may be configured to implement an action based on a context associated with an agricultural object, such as a stage of growth during which, for example, pests and weeds may be more prominent than in other time intervals. 
     At  304 , data representing a subset of agricultural objects may be received. In some cases, each agricultural object may be associated with data representing an identifier. The data representing each of the agricultural objects may be indexed into a data repository. Further, the data representing each of the agricultural objects may be received from, or otherwise originate at, a precision agricultural management platform, which may include one or more processors configured to analyze sensor data (e.g., image data) captured from one or more sensors at, for example, an agricultural treatment delivery system. The sensor data may be analyzed to validate recently captured sensor data (e.g., for an agricultural object) correlates with at least a subset of indexed agricultural object data (e.g., previously sensed data for a specific agricultural object). Also, the sensor data may be analyzed to determine a stage of growth or any other agriculturally-related condition for which a treatment may be applied or delivered. Further, the image data may be used to form a modified or predicted image of an agricultural object at the precision agricultural management platform. 
     As an agricultural treatment delivery system traverses adjacent to arrangements of agricultural objects (e.g., fruit trees), sensed image data from one or more cameras may be compared to data representing a predicted image of the agricultural object. The predicted image may be derived at a precision agricultural management platform to predict a change in an image or physical appearance (or any other characteristic) based on predicted growth of an agricultural object. The predicted image then may be used to detect the corresponding agricultural object in a geographic boundary to which a treatment may be applied. 
     At  306 , a mobility platform may be activated to control autonomously motion and/or position of an agricultural treatment delivery vehicle. A mobility platform may be configured to implement a map, which may include data configured to position one or more emitters of an agricultural projectile delivery system adjacent to an agricultural object within a geographic boundary. Hence, a map may include data specifying a location of an indexed agricultural object, and can be used to navigate a vehicle autonomously to align an emitter with an agricultural object to deliver a treatment. 
     At  308 , an agricultural object may be detected based on or in association with one or more sensors (e.g., one or more image capture devices). Image data of an agricultural object may be generated to form an imaged agricultural object. Then, the imaged agricultural object may be correlated to data representing an indexed agricultural object in a subset of agricultural objects. A correlation may validate that the imaged agricultural object is a same object as described in data associated with the indexed agricultural object. Further, a spatial position of an imaged agricultural object may be correlated to a position and/or an orientation of an emitter. 
     At  310 , an emitter from a subset of one or more emitters may be selected to perform an action. Further, a corresponding action to be performed in association with a particular agricultural object may be identified, the agricultural object being an actionable object (e.g., an agricultural object for which an action is perform, whether chemical or mechanical, such as robotic pruners or de-weeding devices). In some cases, an optical sight associated with an emitter may be identified, and a corresponding action may be associated with the optical sight to determine a point in time to activate emission of an agricultural projectile. 
     At  312 , an agricultural treatment may be emitted as a function of a policy. For example, an emitter may be activated to align an emitter to a spatial position (e.g., at which an agricultural object may be disposed). Upon alignment, propulsion of an agricultural projectile may be triggered to intercept an agricultural object. 
     In an implementation in which a vehicle including an agricultural treatment delivery system is controlled manually, logic in association with an agricultural treatment delivery system may be configured to detect displacement of the vehicle and compute a spatial position of an emitter. Further, an agricultural treatment delivery system may be configured to detect a point or a line (e.g., an optical ray) at which a spatial position of an emitter intersects a path specified by a map. The path may be associated with a subset of agricultural objects for which one or more emitters may be configured to perform a subset of actions. Also, a subset of agricultural objects may be detected in association with one or more sensors. A subset of actions may be identified to be performed in association with a subset of agricultural objects, such as a number of blossoms on one or more trees. One or more emitters may be selected autonomously to perform a subset of actions, whereby one or more emitters may emit a subset of agricultural projectiles to intercept a subset of agricultural objects. In one instance, at least two different agricultural projectiles may be emitted to perform different actions. 
     In a vehicle that includes an agricultural treatment delivery system, and is controlled autonomously, logic in association with an agricultural treatment delivery system may be configured to generate control signals (e.g., at a mobility platform) to drive the vehicle autonomously, compute a spatial position of the vehicle relative to, for example, an agricultural object, and calculate a vehicular trajectory to intersect a path based on, for example, data representing a map. Further, a spatial position of an emitter may be determined to be adjacent to a path specified by the map, the path also being associated with a subset of agricultural objects for which one or more emitters are configured to perform a subset of actions. In some examples, a rate of displacement of the vehicle may be adjusted autonomously to, for example, enhance accuracy, an amount of dosage, or the like. Upon detect a subset of agricultural objects in association with one or more sensors, one or more emitters may be configured to emit a subset of agricultural projectiles to intercept the subset of agricultural objects at the rate of displacement. 
     In at least one implementation, control signals to drive the vehicle autonomously may be supplemented by receiving a first subset of data representing a vehicular trajectory, the data being generating at a teleoperator controller. One or more emitters may emit a subset of agricultural projectiles responsive to a second subset of data originating at the teleoperator controller. 
       FIG. 4  is a functional block diagram depicting a system including a precision agricultural management platform communicatively coupled via a communication layer to an agricultural treatment delivery vehicle, according to some examples. Diagram  400  depicts a mobility controller  447  disposed in an agricultural treatment delivery vehicle  430 , which, in turn, may include any number of sensors  470  of any type. One or more sensors  470  may be disposed within, or coupled to, either mobility controller  447  or an agricultural treatment delivery system  420 , or both. Sensors  470  may include one or more Lidar devices  472 , one or more cameras  474 , one or more radars  476 , one or more global positioning system (“GPS”) data receiver-sensors, one or more inertial measurement units (“IMUs”)  475 , one or more odometry sensors  477  (e.g., wheel encoder sensors, wheel speed sensors, and the like), and any other suitable sensors  478 , such as infrared cameras or sensors, hyperspectral-capable sensors, ultrasonic sensors (or any other acoustic energy-based sensor), radio frequency-based sensors, etc. 
     Other sensor(s)  478  may include air flow-related sensors to determine magnitudes and directions of ambient airflow relative to agricultural treatment delivery system  420  and one or more emitters configured to emit an agricultural projectile  412 . Air flow-related sensors may include an anemometer to detect a wind speed and a wind vane to detect wind direction. Values of wind speed and direction may be determined relative to a direction and velocity of agricultural treatment delivery vehicle  430 , and may further be used to adjust a time at which to emit agricultural projectile  412  and/or modify a trajectory as a function of windage (e.g., wind speed and direction). In some cases, wheel angle sensors configured to sense steering angles of wheels may be included as odometry sensors  477  or suitable sensors  478 . Sensors  470  may be configured to provide sensor data to components of mobility controller  447  and/or agricultural treatment delivery vehicle  430 , as well as to elements of precision agricultural management platform  401 . As shown in diagram  400 , mobility controller  447  may include a planner  464 , a motion controller  462 , a motion estimator/localizer  468 , a perception engine  466 , and a local map generator  440 . Note that elements depicted in diagram  400  of  FIG. 4  may include structures and/or functions as similarly-named elements described in connection to one or more other drawings. 
     Motion estimator/localizer  468  may be configured to localize agricultural treatment delivery vehicle  430  (i.e., determine a local pose) relative to reference data, which may include map data, route data, and the like. Route data may be used to determine path planning over one or more paths (e.g., unstructured paths adjacent to one or more plants, crops, etc.), whereby route data may include paths, path intersections, waypoints (e.g., reflective tape or other visual fiducial markers associated with a trellis post), and other data. As such, route data may be formed similar to road network data, such as RNDF-like data, and may be derived and configured to navigate paths in an agricultural environment. In some cases, motion estimator/localizer  468  may be configured to identify, for example, a point in space that may represent a location of agricultural treatment delivery vehicle  430  relative to features or objects within an environment. Motion estimator/localizer  468  may include logic configured to integrate multiple subsets of sensor data (e.g., of different sensor modalities) to reduce uncertainties related to each individual type of sensor. According to some examples, motion estimator/localizer  468  may be configured to fuse sensor data (e.g., Lidar data, camera data, radar data, etc.) to form integrated sensor data values for determining a local pose. According to some examples, motion estimator/localizer  468  may retrieve reference data originating from a reference data repository  405 , which may include a map data repository  405   a  for storing 2D map data, 3D map data, 4D map data, and the like. Motion estimator/localizer  468  may be configured to identify at least a subset of features in the environment to match against map data to identify, or otherwise confirm, a position of agricultural treatment delivery vehicle  430 . According to some examples, motion estimator/localizer  468  may be configured to identify any amount of features in an environment, such that a set of features can one or more features, or all features. In a specific example, any amount of Lidar data (e.g., most or substantially all Lidar data) may be compared against data representing a map for purposes of localization. In some cases, non-matched objects resulting from a comparison of environment features and map data may be classify an object as a dynamic object. A dynamic object may include a vehicle, a farm laborer, an animal, such as a rodent, a bird, or livestock, etc., or any other mobile object in an agricultural environment. Note that detection of dynamic objects, including obstacles, such as fallen branches in a path, may be performed with or without map data. In particular, dynamic or static objects may be detected and tracked independently of map data (i.e., in the absence of map data). In some instances, 2D map data and 3D map data may be viewed as “global map data” or map data that has been validated at a point in time by precision agricultural management platform  401 . As map data in map data repository  405   a  may be updated and/or validated periodically, a deviation may exist between the map data and an actual environment in which agricultural treatment delivery vehicle  430  is positioned. Therefore, motion estimator/localizer  468  may retrieve locally-derived map data generated by local map generator  440  to enhance localization. For example, locally-derived map data may be retrieved to navigate around a large puddle of water on a path, the puddle being omitted from global map data. 
     Local map generator  440  is configured to generate local map data in real-time or near real-time. Optionally, local map generator  440  may receive static and dynamic object map data to enhance the accuracy of locally-generated maps by, for example, disregarding dynamic objects in localization. According to at least some embodiments, local map generator  440  may be integrated with, or formed as part of, motion estimator/localizer  468 . In at least one case, local map generator  440 , either individually or in collaboration with motion estimator/localizer  468 , may be configured to generate map and/or reference data based on simultaneous localization and mapping (“SLAM”) or the like. Note that motion estimator/localizer  468  may implement a “hybrid” approach to using map data, whereby logic in motion estimator/localizer  468  may be configured to select various amounts of map data from either map data repository  405   a  or local map data from local map generator  440 , depending on the degrees of reliability of each source of map data. Therefore, motion estimator/localizer  468  may use out-of-date map data in view of locally-generated map data. 
     In various examples, motion estimator/localizer  468  or any portion thereof may be distributed in or over mobility controller  447  or agricultural treatment delivery system  420  in any combination. In one example, motion estimator/localizer  468  may be disposed as motion estimator/localizer  219   a  in agricultural treatment delivery system  420 . Also, agricultural treatment delivery system  420  may also include sensors  470 . Therefore, agricultural treatment delivery system  420  may be configured to autonomously apply treatments to agricultural objects independent of mobility controller  447  (i.e., agricultural treatment delivery vehicle  430  may navigate manually). In another example, agricultural treatment delivery vehicle  430  may navigate autonomously. Hence, motion estimator/localizer  468  may be disposed in either mobility controller  447  or agricultural treatment delivery system  420 , with its functionalities being shared by mobility controller  447  and agricultural treatment delivery system  420 . According to some examples, motion estimator/localizer  468  may include NavBox logic  469  configured to provide functionalities and/or structures as described in U.S. Provisional Patent Application No. 62/860,714 filed on Jun. 12, 2019 and titled “Method for Factoring Safety Components into a Software Architecture and Software and Apparatus Utilizing Same.” 
     Perception engine  466  may be configured to, for example, assist planner  464  in planning routes and generating trajectories by identifying objects of interest (e.g., agricultural objects) in a surrounding environment in which agricultural treatment delivery vehicle  430  is traversing. As shown, perception engine  466  may include an object detector  442   a  configured to detect and classify an agricultural object, which may be static or dynamic. Examples of classifications with which to classify an agricultural object includes a class of leaf, a class of bud (e.g., including leaf buds and fruit buds), a class of blossom, a class of fruit, a class of pest (e.g., insects, rodents, birds, etc.), a class of disease (e.g., a fungus) a class of a limb (e.g., including a spur as an object), a class of obstacles (e.g., trellis poles and wires, etc.), and the like. Object detector  442   a  may be configured to distinguish objects relative to other features in the environment, and may be configured to further identify features, characteristics, and attributes of an agricultural object to confirm that the agricultural object relates to an indexed agricultural object and/or policy stored in memory  421 . Further, perception engine  466  may be configured to assign an identifier to an agricultural object that specifies whether the object is (or has the potential to become) an obstacle that may impact path planning at planner  464 . Although not shown in  FIG. 4 , note that perception engine  466  may also perform other perception-related functions, predicting “freespace” (e.g., an amount of unencumbered space about or adjacent an agricultural object) or whether a subset of agricultural objects (e.g., leaves) may obstruct agricultural projectile trajectories directed to another subset of agricultural objects (e.g., blossoms) to calculate alternative actions or agricultural projectile trajectories. In some examples, object detector  442   a  may be disposed in SenseBox logic  442 , which may be configured to provide functionalities and/or structures as described in U.S. Provisional Patent Application No. 62/860,714 filed on Jun. 12, 2019 and titled “Method for Factoring Safety Components into a Software Architecture and Software and Apparatus Utilizing Same.” 
     Planner  464  may be configured to generate a number of candidate vehicle trajectories for accomplishing a goal of traversing within a geographic boundary via a number of available paths or routes, and planner  464  may further be configured to evaluate candidate vehicle trajectories to identify which subsets of candidate vehicle trajectories may be associated with higher degrees of confidence levels of providing collision-free paths adjacent one or more plants. As such, planner  464  can select an optimal vehicle trajectory based on relevant criteria for causing commands to generate control signals for vehicle components  450  (e.g., actuators or other mechanisms). Note that the relevant criteria may include any number of factors that define optimal vehicle trajectories, the selection of which need not be limited to reducing collisions. In some cases, at least a portion of the relevant criteria can specify which of the other criteria to override or supersede, while maintain optimized, collision-free travel. In some examples, planner  464  may be include ActionBox logic  465 , which may be configured to provide functionalities and/or structures as described in U.S. Provisional Patent Application No. 62/860,714 filed on Jun. 12, 2019 and titled “Method for Factoring Safety Components into a Software Architecture and Software and Apparatus Utilizing Same.” 
     In some examples, motion controller  462  may be configured to generate control signals that are configured to cause propulsion and directional changes at the drivetrain and/or wheels of agricultural treatment delivery vehicle  430 . In this example, motion controller  462  is configured to transform commands into control signals (e.g., velocity, wheel angles, etc.) for controlling the mobility of agricultural treatment delivery vehicle  430 . In the event that planner  464  has insufficient information to ensure a confidence level high enough to provide collision-free, optimized travel, planner  464  can generate a request to teleoperator controller  404  (e.g., a teleoperator computing device), for teleoperator support. In some examples, motion controller  462  may be include SafetyBox logic  443 , which may be configured to provide functionalities and/or structures as described in U.S. Provisional Patent Application No. 62/860,714 filed on Jun. 12, 2019 and titled “Method for Factoring Safety Components into a Software Architecture and Software and Apparatus Utilizing Same.” 
     Autonomous vehicle service platform  401  includes reference data repository  405 , a map updater  406 , and an object indexer  410 , among other functional and/or structural elements. Note that each element of autonomous vehicle service platform  401  may be independently located or distributed and in communication with other elements in autonomous vehicle service platform  401 . Further, any component of autonomous vehicle service platform  401  may independently communicate with the agricultural treatment delivery vehicle  430  via the communication layer  402 . Map updater  406  is configured to receive map data (e.g., from local map generator  440 , sensors  460 , or any other component of mobility controller  447 ), and is further configured to detect deviations, for example, of map data in map data repository  405   a  from a locally-generated map. Map updater  406  may be configured to update reference data within repository  405  including updates to 2D, 3D, and/or 4D map data. Object indexer  410  may be configured to receive data, such as sensor data, from sensors  470  or any other component of mobility controller  447 . According to some embodiments, a classification pipeline of object indexer  410  may be configured to annotate agricultural objects (e.g., manually by a human and/or automatically using an offline labeling algorithm), and may further be configured to train a classifier (e.g., on-board agricultural treatment delivery vehicle  430 ), which can provide real-time classification of agricultural object types during autonomous operation. In some examples, object indexer  410  may be configured to implement computer vision and machine learning algorithms to construct and maintain a spatial semantic model (e.g., at resolutions of sub-centimeter, or less) and/or a time-series model of plant physiology and state-of-growth. Data representing any of these models may be linked to, or disposed in, data representing indexed agricultural object data. 
     Agricultural treatment delivery system  420  may include hardware or software, or any combination thereof, and may include a memory  421 , a motion estimator/localizer  219   a , a target acquisition processor  422 , a trajectory processor  424 , an emitter propulsion subsystem  426 , and calibration logic  409 . Memory  421  may be configured to store policy data to specify an action or treatment for an associated indexed agricultural object, and may also store indexed agricultural object data (e.g., describing a specific agricultural object of interest, including identifier data and image data, which may be predicted). Motion estimator/localizer  219   a  may be configured to determine a position of agricultural treatment delivery system  420  or an emitter relative to an agricultural object targeted for treatment. Target acquisition processor  422  may be configured to sense or otherwise detect an agricultural object, such as a blossom, that may be identified in association with indexed agricultural object data. Hence, target acquisition processor  422  may acquire an agricultural object as a target for treatment, whereby an acquired agricultural object may be detected in a subset of pixels in image data. Trajectory processor  424  may be configured to track an acquired agricultural object as a subset of pixels in image data relative to, for example, an optical sight. In event the tracked subset of pixels aligns with the optical sight, trajectory processor  424  may generate a control signal to initiate delivery of a payload (i.e., a treatment) as agricultural projectile  412 . Responsive to receiving a control signal, emitter propulsion subsystem  426  may be configured to propel agricultural projectile  412  toward a target. Calibrator  409  may include logic configured to perform calibration of various sensors, such as image sensors, of the same or different types. In some examples, calibrator  409  may be configured to compute a trajectory direction (e.g., in Cartesian space (x, y, z) and/or orientation of an emitter (e.g., roll, pitch and yaw). As such, a position and orientation of an emitter may be calibrated to intercept a target, such as visual fiducial marker or a laser light beam on a surface, whereby a pixel associated with an optical sight may cause an agricultural projectile  412  to be emitted when a subset of pixels of target in an image aligns with a subset of pixels associate with an optical sight. In this example, alignment of an optical sight to a target may be in line with an optical ray extending through the optical sight. 
       FIG. 5  is a diagram depicting another example of an agricultural treatment delivery system, according some examples. Diagram  500  depicts an agricultural projectile delivery system  581  implemented as an agricultural treatment delivery system, whereby agricultural projectile delivery system  581  may be configured to detect an agricultural object, identify a course of action (e.g., based on policy data), track an image of the agricultural object, and emit an agricultural projectile  512  to intercept an agricultural object as a target. Agricultural projectile delivery system  581  may include one or more image capture devices, such as a camera  504  and a Lidar  505 , the imaged sensor data from each may or may not be integrated or “fused,” according to various examples. As shown, one or more image capture devices  504  and  505  may be configured to capture an image of an agricultural environment  501  in a field of view of, for example, image capture device  504 , the captured image being received into agricultural projectile delivery system  581  via, for example, sensor data  576 , as agricultural environment image  520 . 
     In accordance with some examples, agricultural projectile delivery system  581  includes one or more emitters  503  disposed in a field of view between image capture device  504  and objects of interest, such as agricultural objects disposed in agricultural environment  501 . Therefore, emitters  503  may be presented as image data  511  in agricultural environment image  520 , the image data  511  of emitters thereby occluding images of one or more agricultural objects in agricultural environment  501 . In examples in which image data  511  obscures or occludes a portion of agricultural environment  501 , agricultural projectile delivery system  581  may be configured to generate optical sights  513  that, at least in some cases, may be coaxial with an orientation of an aperture of a corresponding emitter. For example, an optical sight  513   a  may be centered coaxially about a line  514  coincident with a trajectory direction of a corresponding aperture. Further, line  514  may be an optical ray extending from at least one pixel in a subset of pixels associated with a center of  513   a  (or any other portion of an optical sight) to a target in agricultural environment  501 . In at least one implementation, an emitter may be a nozzle and an aperture may refer to a nozzle opening. 
     Agricultural environment image  520  includes image data representing one or more agricultural objects, such as objects  522  to  529 . Object  522  is a blossom, object  524  is an open cluster, object  525  is a spur, object  527  is a leaf or other foliage, and object  529  is a portion of a trunk or stem. Other objects—as agricultural objects—based on agricultural applications, associations, and implementations, may be depicted in agricultural environment image  520 , such as a post  532 , a wire  533 , soil  534 , and a marker  531 , among others. Marker  531  may be detected and analyzed to determine positioning information, to facilitate in-situ positioning or calibration, or to perform any other function. 
     Note that image frame  509  and image data  511  of emitters  503  may be affixed to a frame of reference of, for example, an agricultural treatment delivery vehicle (not shown) as it travels in direction  543 , at least in this example. Therefore, objects within image frame  509  including agricultural objects  522  to  529  may traverse agricultural environment image  520  in a direction of image travel  541 . Consequently, agricultural objects for which a treatment may be applied may move toward, for example, an array of optical sights  513  (e.g., to the right in diagram  500 ). 
     Agricultural projectile delivery system  581  may be configured to receive policy data  572  to specify an action, a treatment, or the like for an agricultural object, as well as indexed object data  574  to provide data (including imagery data for comparison) that specifies any number of characteristics, attributes, actions, locations, etc., of an agricultural object. Agricultural projectile delivery system  581  may receive or derive sensor data  576  (e.g., image data, wind speed data, wind direction data, etc.) as well as position data  578  (e.g., a position of an agricultural object). Agricultural projectile delivery system  581  also may be configured to receive any other types of data. 
     Agricultural projectile delivery system  581  is shown to include a target acquisition processor  582 , a trajectory processor  583 , and an emitter propulsion subsystem  585 . In various examples, target acquisition processor  582  may be able to identify an agricultural object, such as object  522 , that may be correlatable to a subset of indexed agricultural object data  574 , which may include previously-sensed data and data predicting an image of the identified agricultural object with a predicted amount of growth. Among other things, a predicted image may facilitate image-based identification of a uniquely identified agricultural object among many others in geographic location, such as an orchard. Further, target acquisition processor  582  may detect whether policy data  572  specifies whether an action is to be taken. If not, one or more sensors may monitor and capture data regarding a non-targeted identified agricultural object. As a non-targeted object, however, it need not be tracked as a target (e.g., identifying an optical sight may be omitted as well as a treatment). In some cases, an identified agricultural object may be associated with an action, such as object  522 . Trajectory processor  583  may be configured to select an optical sight for implementing an action, and may be further configured to track an identified agricultural object indicated as requiring treatment as its image data traverses in direction  541 . Trajectory processor  583  may also be configured to predict an emission parameter (e.g., emission time) at which an agricultural object aligns with an optical sight. At a detected emission time, trajectory processor  583  may generate a control signal to transmit to emitter propulsion subsystem  585 , which, in turn may activate an emitter to propel agricultural projectile  512  to intercept object  522  at a calculated time. 
       FIG. 6  is an example of a flow diagram to align an emitter to a target autonomously, according to some embodiments. Flow  600  begins at  602 , at which sensor data representing presence of agricultural objects disposed in an agricultural environment may be received. In some examples, an image capture device, such as a camera, may capture an image of one or more agricultural objects in a subset of agricultural objects. Also, image data representing a number of agricultural objects in a field of view of an image capture device may be received at  602 . Consider that the one or more agricultural objects are blossoms. The image capture device may receive light (e.g., reflective sunlight) from an agricultural object in a field of view of image capture device. Image data representing the agricultural object may be captured at any rate (e.g., 30 frames a minute, or fewer or more). In one example, reflective light may be received from an agricultural object in one or more time intervals during which reflective light is visible (e.g., within a visible light spectrum, such as when sunlight is available). In other examples, the reflected light received into an image capture device may be infrared light or any other spectrum of light. As such, an agricultural treatment delivery system may operate in the absence of sunlight. In at least one embodiment, one or more emitters may be disposed in between an image capture device and an agricultural object. For example, one or more emitters may be disposed or positioned in a field of view of a camera, whereby an aperture of an emitter (e.g., aperture of a nozzle) may be aligned coaxially with an optical ray corresponding to a pixel at the center of aperture of an emitter in a captured image. In other examples, an aperture of an emitter need not be aligned coaxially with an optical ray. 
     At  604 , an agricultural object may be identified as, for example, a bloom that is associated with indexed agricultural object data, which may include previously captured image data and an identifier that uniquely distinguishes the identified agricultural object from other agricultural objects throughout, for example, an orchard. A subset of agricultural objects may also be captured as image data in a field of view. 
     At  606 , a determination is made as to whether an identified agricultural object is correlatable to indexed data (e.g., previously sensed data regarding the agricultural object that may be processed and indexed into a data arrangement stored in a data repository). In no, flow  600  moves to  612 . If yes, flow  600  may move to  608  to determine, optionally, a spatial location of an identified agricultural object, as a function of a position of an agricultural treatment delivery vehicle or an emitter. At  610 , a spatial location of the identified agricultural object may be compared to location data in indexed agricultural object data to analyze whether the identified agricultural object is correlated to indexed data (i.e., the identified agricultural object and indexed agricultural object data relate to the same object). 
     At  612 , an action may be associated to data representing the identified agricultural object. For example, policy data may be linked to indexed agricultural object data, which may specify a first policy to germinate king blossoms and a second policy to terminate lateral blossoms, whereby these two policies may be implemented individually or in combination (simultaneously or nearly simultaneously). For example, consider that an identified agricultural object is identified using indexed data or other image processing that predicts a classification for the identified agricultural object, whereby the identified agricultural object is predicted to be a “king blossom.” Therefore, an action relating to the first policy (e.g., germination) may be linked to the identified agricultural object to perform that action. Note that a subset of agricultural objects of the same or different classifications (or types) may be detected in a field of view and correlated to one or more corresponding actions to be performed in association with one or more emitters. 
     At  614 , an identified agricultural object may be locked onto and tracked as a target for applying a treatment. In some cases, one or more optical sights may configured to detect alignment with one or more identified agricultural objects. 
     At  616 , each optical sight may be predicted to align with an associated agricultural object at  616 , the optical sight being associated with an emitter. In particular, an optical sight may be selected to align with a target relative to other optical sights, the optical sight being associated with an emitter for applying a treatment to a corresponding identified agricultural object. In some cases, an emitter is oriented to emit an emission parallel (e.g., coaxially) with an optical ray extending from an optical sight to a target, the optical sight being associated with one or more pixels of an image capture device. Further, one or more agricultural objects may be tracked relative to one or more optical sights. For example, reflective light from one or more of the agricultural objects may be tracked in a field of view of an image captured by a camera. A field of view of an image capture device may be a parameter (e.g., an angle) through which observable light or electromagnetic radiation may be captured in an image, according to some examples. Also, the reflective light from an agricultural object can be captured in an image and tracked in association with a visible image portion (e.g., a non-occluded image portion). 
     At  618 , a trajectory of an agricultural projectile may be computed (e.g., relative to an emission parameter). In other examples, a trajectory of an agricultural projectile may be computed to adjust an orientation of an emitter, at least in one instance. 
     At  620 , a value of an elapsed time to alignment of an optical sight to an agricultural object may be calculated and tracked. Based on a velocity of an agricultural treatment delivery vehicle, a time to emit an agricultural projectile may be computed and tracked. Hence, tracking an optical sight relative to an agricultural object may be a function of a rate of displacement of one or more emitters or a vehicle (e.g., relative to the soil or the agricultural environment). Further, a portion of the value of the elapsed time may be calculated. The portion of the elapsed time value may describe an amount of time during which the agricultural object is associated with an occluded image portion. 
     At  622 , a determination is made as to whether any of sensor data detects a variance, such as a change in emitter altitude (e.g., a bump or raised elevation, or dip or depression) or any other change in sensor data, such as a variation in vehicle speed. If there is a variance, a trajectory may be recomputed at  624  (e.g., recomputing an emission parameter associated with the trajectory). For example, if a change in emitter altitude changes relative to the ground, an initial optical sight may be misaligned. Thus, another optical sight may be selected at  626 . But if there is no variance, flow  600  moves from  622  to  628 . 
     At  628 , an agricultural object can be predicted to align with an optical sight to form a predicted emission parameter, which may be monitored to detect alignment of an optical sight and a target. The predicted emission parameter may be tracked in association with agricultural object. For example, a predicted emission parameter may be a predicted emission time, either a duration or elapsed amount of time, or a point in time at which alignment occurs, thereby providing a trajectory via, for example, an optical ray. Further, alignment of an agricultural object with an optical sight may be detected at the predicted emission parameter. 
     At  630 , an emitter is activated to apply an action based on a predicted emission parameter. Thus, emission of an agricultural projectile may be triggered at a predicted emission time. In one example, an emission time may specify a time at which a pixel associated with an optical sight is aligned with an optical ray that extends from the pixel to at least a portion of a targeted agricultural object. 
       FIGS. 7A and 7B  depict examples of data generated to identify, track, and perform an action for one or more agricultural objects in an agricultural environment, according to some examples.  FIG. 7A  is a diagram depicting an image frame  700  in which an agricultural environmental image  720   a  includes agricultural objects identified as targets  722   a ,  722   b ,  722   c ,  722   d ,  722   e ,  722   f ,  7   ddg ,  722   h ,  722   j ,  724   a , and  724   b . In various examples, agricultural environmental image  720   a  may be presented to a user in a graphical user interface (not shown), or may represent data calculations, derivations, functions, and the like based on image data and other data. Agricultural environmental image  720   a  also includes image data  711   a  representing emitters and corresponding optical sights, such as optical sights  726   a ,  726   b ,  726   c ,  726   d ,  726   e ,  726   f , and  726   g.    
     An agricultural projectile delivery system (not shown) may be configured to identify and select optical sight  726   a  (and corresponding emitter) to apply a treatment to target  722   a . Further, agricultural projectile delivery system may be configured to identify and select optical sights  726   b ,  726   c ,  726   d ,  726   e ,  726   f ,  726   g , and  726   g  to emit agricultural projectiles to targets  722   e ,  722   f ,  722   b ,  722   g ,  722   c ,  722   h , and  722   d , respectively. Note that optical sight  726   g  may be configured to propel an agricultural projectile to both target  722   h  and target  722   d  at, for example, different alignments. Note that dotted lines  723  may represent data configured to specify a distance  727  (e.g., a number of pixels) or a time until a target aligns with an optical sight. Solid lines  725  represent data configured to specify a distance or time subsequent to a treatment, as applied to targets  724   a  and  724   b . Both targets  724   a  and  724   b  are depicted with crosshatch to signify that one or more treatments have been performed. Any of the above-described actions may be performed by hardware and/or software that provide functionality to an agricultural projectile delivery system.  FIG. 7A  depicts targets  722   g ,  724   a , and  724   b  being occluded by image data  711   a  of emitters at a first point in time, t 1 . 
       FIG. 7B  is a diagram depicting an image frame  750  depicting an image frame  750  in which an agricultural environmental image  720   b  includes agricultural objects identified as targets  722   a ,  722   b ,  722   c ,  722   d ,  722   e ,  722   f ,  7   ddg ,  722   h ,  722   j ,  724   a , and  724   b , as well as targets  772   x ,  772   y , and  772   z , whereby targets  772   e ,  772   b ,  772   g , and  772   j  are occluded or partially occluded by image data  711   b  of an array of emitters. Agricultural environmental image  720   b  depicts a state in which targets  724   a ,  724   b ,  772   f ,  772   e ,  772   g ,  772   h , and  772   j  have received treatment by at least at time, t 2 . These treated targets are depicted in crosshatch. Targets identified at time, t 2 , including targets  772   x ,  772   y , and  772   z , may be calculated to receive treatment at or within time, t 3 . As such, targets  772   x ,  772   y , and  772   z  have been associated with optical sights, such as  776   z , to detect alignment and trigger propulsion of agricultural projectiles to apply various treatments. 
       FIG. 7C  is a diagram depicting parameters with which to determine activation of an emitter to apply a treatment, according to some examples.  FIG. 7C  depicts an image capture device  774  capturing image data representing an agricultural environment  770 , which includes a target  777   z , and image data representing an array of emitters  711   c  disposed in a field of view of image capture device  774 . Hence, image capture device  774  may be configured to generate an agricultural environment image  720   a  within an image frame  750   a . Logic of an agricultural projectile delivery system may be configured to identify visible image fields  785   a  and  785   b  in which one or more agricultural objects, such as target  777   za , are visible (i.e., not occluded). Therefore, digitized or pixelated image data associated with targets, such as target  777   za , may be observed, identified, and tracked, among other things. Logic of an agricultural projectile delivery system also may be configured to identify occluded image field  787   a  associated with image data  711   bb  of emitter  711   c . While pixelated data associated with target  777   za  may be tracked in visible image field  785   a , it may become occluded as motion moves the image of target  777   za  to an optical sight  776   za.    
     In at least one example, an agricultural projectile delivery system may be configured to identify target  777   za  and one or more pixels association therewith (e.g., as a pixelated target  777   zb ). The agricultural projectile delivery system may also be configured to derive a predicted emission parameter  790  (e.g., a predicted time) that may be used determine a point in time to activate an emitter to propel a projectile to a target. Further, an agricultural projectile delivery system may also be configured to determine a visible parameter  791   a  during which visible target pixels  780   a  may be analyzed to track, evaluate, and modify predicted emission parameter  790  (e.g., changes in vehicle speed, a gust of wind, or the like). The agricultural projectile delivery system may computationally predict an occluded parameter  791   b  during which occluded target pixels  780   b  may not be visible due to occluded image field  787   a  associated with image data  711   b  of the emitters. As pixels of target  777   za  travel to optical sight  776   za  in an occluded image field  787 , a rate at which a pixel of a target  777   za  may align with optical sight  776   za  may be modified based on sensor data (e.g., vehicle speed), which, in turn, may modify data values representing occluded parameter  791   b , thereby modifying an emission time. 
       FIG. 8  is a diagram depicting a perspective view of an agricultural projectile delivery vehicle configured to propel agricultural projectiles, according to some examples. Diagram  800  depicts an agricultural projectile delivery vehicle  810  traveling in a direction of motion  813  at a distance  815 . Further, diagram  800  depicts an agricultural projectile delivery system  811  detecting blossoms as targets  722   f ,  724   b , and  724   a  of  FIG. 7B  and propels agricultural projectiles  812 ,  814 , and  813  to intercept respective targets. In some examples, each of agricultural projectiles  812 ,  814 , and  813  each may be emitted at any angle along a trajectory that lies in a plane that include an optical ray, such as optical ray  877 . 
       FIG. 9  is a diagram depicting an example of trajectory configurations to intercept targets autonomously using an agricultural projectile delivery vehicle, according to some examples. Diagram  900  depicts an agricultural projectile delivery vehicle  910  including an agricultural projectile delivery system  952   a  configured to propel agricultural projectiles, such as agricultural projectile  912 , at any angle  909  relative to plane  911 . One or more emitters of agricultural projectile delivery system  952   a  may be configured to propel agricultural projectiles via one or more trajectories  999  to intercept any target  722   f  relative to any height above ground. As shown, agricultural projectile delivery system  952   a  may be configured to propel agricultural projectile  912  with a force having a vertical component (“Fvc”) and a horizontal component (“Fhc”). As shown, the vertical component of the propulsion force may be in a direction opposite than the force of gravity (“Fg”). Note that horizontal component (“Fhc”) may have a magnitude sufficient to propel agricultural projectile  912  over a horizontal distance to target  722   f . According to some examples, agricultural projectile delivery vehicle  910  may include an agricultural projectile delivery system  952   b  configured to apply treatments via one or more trajectories  954  within a space  950 . Therefore, agricultural projectile delivery vehicle  910  may be configured to identify agricultural objects along both sides of agricultural projectile delivery vehicle  910 . For example, agricultural projectile delivery vehicle  910  may be configured to identify groups  990  and  991  of agricultural objects (e.g., fruit trees) and may simultaneously apply a treatment or different treatments to either side as agricultural projectile delivery vehicle  910  traverses a path (in the X-direction) between groups  990  and  991  of agricultural objects. 
       FIG. 10  is a diagram depicting examples of different emitter configurations of agricultural projectile delivery systems, according to some examples. Diagram  1000  depicts an agricultural projectile delivery vehicle  1010  having at least two exemplary configurations, each of which may be implemented separately (e.g., at different times). Configuration  1001  includes an agricultural projectile delivery system  1052   a  having any number of emitters configured to emit agricultural projectiles substantially horizontally (e.g., orthogonal or substantially orthogonal to a direction of gravitational force) to target agricultural objects  722   f  Configuration  1002  includes a boom  1060  configured to support an agricultural projectile delivery system  1012  configured to identify, monitor, track, and apply a treatment via an agricultural projectile to one or more agricultural objects constituting row crops, such as soybean plant  1061   a  and soybean plant  1061   b , or portions thereof 
       FIG. 11  is a diagram depicting yet another example of an emitter configuration of an agricultural projectile delivery system, according to some examples. Diagram  1100  depicts an agricultural projectile delivery vehicle  1110  having an exemplary configuration in which emitters may be configured to propel an agricultural projectile via any trajectory  1112  from an encompassing structure  1125 , which may be configured to apply treatments to any nut or fruit tree having three dimensions of growth. As shown, encompassing structure  1125  may have articulating members that can be positioned in either arrangement  1160   a  or  1160   b . The articulating members may include emitters to apply one or more treatments to one or more agricultural objects associated with a three dimensional vegetative structure, such as an orange tree or walnut tree. The configuration shown in diagram  1100  is not limiting and any configuration of agricultural projectile delivery system may be used to apply treatments to tree  1190 . 
       FIGS. 12 and 13  are diagrams depicting examples of a trajectory processor configured to activate emitters, according to some examples. Diagram  1200  of  FIG. 12  includes a rear view of an array of emitters  1211  and a side view of an agricultural projectile delivery system  1230 . As shown in the rear view, array of emitters  1211  is disposed in a field of view of an image capture device  1204 . In particular, array of emitters  1211  may be interposed between a scene being imaged (e.g., agricultural environment  1210 , which includes targets  1222   a ) and image capture device  1204 . In some examples, image capture device  1204  may be configured to be “in-line” with optical sights  1214  to determine alignment of an optical sight with a target, such as target  1222   a . In diagram  1200 , an array of emitters  1211  include groups  1219  of emitters that are each positioned offset in an X-direction and a Y-direction. Note, however, arrangements of emitters into groups  1219  of emitters is a non-limiting example as array of emitters  1211  may each be arranged in any position and in any orientation. Also shown in the rear view, array of emitters  1211  is opaque (e.g., depicted as a shaded region) and occludes images in agricultural environment  1210 . 
     Diagram  1200  also depicts an agricultural environment image  1220 , which may be generated by image capture device  1204  and disposed within image frame  1250 . Agricultural environment image  1220  includes image data  1279  representing emitters and corresponding optical sights, such as an optical sights  1226   a . As array of emitters  1211  traverses in agricultural environment  1210 , images of target  1222   a , such as target image  1222   b  in a first position may travel to a second position as target image  1222   c  (e.g., a position at which target  1222   a  in agricultural environment  1210  may be occluded). 
     Diagram  1200  also includes a trajectory processor  1283 , which may be configured to track positions of target image  1222   b  and to determine a distance  1227  (or any other parameter, including time) at which an image of the target (i.e., target image  1222   c ) aligns with optical sight  1226   a . Target processor  1283  may also be configured to calculate and monitor deviations of predicted distance (“D”)  1203  during which target  1222  may be occluded by array of emitters  1211 . Predicted distance  1203  may correlate to a time from which target  1222   a  becomes occluded until alignment with an optical sight  1226   a . Trajectory processor  1283  may detect a point in time at which target image  1222   c  aligns (or is predicted to align) with optical sight  1226   a , and in response, trajectory processor may generate control data  1229 . Control data may transmitted to an emission propulsion system (not shown) in emitter  1230 . In addition, control data  1229  may include executable instructions or any data configured to activate emitter  1213   b  to propel agricultural project  1212  to intercept  1222   d.    
       FIG. 13  depicts a trajectory processor  1383  configured to operate similarly or equivalently as trajectory processor  1283  of  FIG. 12 . Diagram  1300  of  FIG. 13  includes a rear view of an array of emitters  1311  and a side view of an agricultural projectile delivery system  1330 . As shown in the rear view, array of emitters  1311  may be disposed in a field of view of an image capture device  1304 . Therefore, array of emitters  1311  may be positioned between an agricultural environment  1310 , which includes targets  1322   a , and image capture device  1304 . In diagram  1300 , an array of emitters  1311  are arranged in a line (or substantially in a line) in a Y-direction. Note that in some cases, array of emitters  1311  may be arranged in an X-direction. In this example, a predicted distance (“D”)  1303  during which a target  1322   a  may be occluded may be less than, for example, predicted distance  1203  of  FIG. 12 , thereby enhancing or prolonging visibility of target  1322   a  as it traverses within an image. According to other examples, each emitter may be arranged in any position and in any orientation. Also shown in the rear view, array of emitters  1311  is depicted with shading to represent that is may be opaque, and may occlude images in agricultural environment  1310 . 
     Diagram  1300  also depicts an agricultural environment image  1320 , generated by image capture device  1304  and disposed within image frame  1350 . Agricultural environment image  1320  includes image data  1379  representing emitters and corresponding optical sights, such as an optical sight  1326   a . As array of emitters  1311  traverses in an agricultural environment  1310 , images of target  1322   a , such as target image  1322   b  in a first position may travel to a second position as target image  1322   c  (e.g., a position at which target  1322   a  in agricultural environment  1310  may be occluded). 
     Diagram  1300  also includes a trajectory processor  1383 , which may be configured to track positions of target image  1322   b  and to determine a distance  1327  (or any other parameter, including time) at which an image of the target (i.e., target image  1322   c ) aligns with optical sight  1326   a . Target processor  1383  may also be configured to calculate and monitor deviations of predicted distance (“D”)  1303  during which target  1322  may be occluded by array of emitters  1311 . Predicted distance  1303  may correlate to a time from which target  1322   a  becomes occluded until alignment with an optical sight  1326   a . Trajectory processor  1383  may detect a point in time at which target image  1322   c  aligns (or is predicted to align) with optical sight  1326   a , and in response, trajectory processor  1383  may generate control data  1329 . Control data may be transmitted to an emission propulsion system (not shown) in emitter  1330 . In addition, control data  1329  may include executable instructions or any data configured to activate emitter  1313   b  to propel agricultural project  1312  to intercept  1322   d.    
       FIG. 14  is a diagram depicting an example of components of an agricultural projectile delivery system that may constitute a portion of an emitter propulsion subsystem, according to some examples. Diagram  1400  includes an agricultural projectile delivery system  1430  that includes a number of emitters  1437  (or portions thereof), as well as at least on in-line camera  1401 , according to some implementations. 
     In one example, agricultural projectile delivery system  1430  may include a storage for compressed gas (“compressed gas store”)  1431 , which may store any type of gas (e.g., air), a gas compressor  1432  to generate one or more propulsion levels (e.g., variable levels of pressure), and a payload source  1433 , which may store any treatment or payload (e.g., a liquid-based payload), such as fertilizer, herbicide, insecticide, etc. In another example, agricultural projectile delivery system  1430  may omit compressed gas store  1431  and gas compressor  1432 , and may include a pump  1434  to generate one or more propulsion levels with which to propel a unit of payload source  1433 . In various implementations, one or more of the components shown in agricultural projectile delivery system  1430  may be included or may be omitted. 
     Agricultural projectile delivery system  1430  may also include any number of conduits  1435  (e.g., hoses) to couple payload source  1433  to a number of activators  1436 , each of which is configured to activate to deliver a unit of payload as, for example, an agricultural projectile, to an identified target. To illustrate operation, consider that control data  1470  is received into agricultural projectile delivery system  1430  to launch one or more units of treatment or payload. Logic in agricultural projectile delivery system  1430  may be configured to analyze control data  1470  to that identify activator  1441  is to be triggered at a point in time, or at a position that aligns a corresponding emitter  1442  to target  1460 . When activated, activator  1441  may release an amount of payload (e.g., a programmable amount) with an amount of propulsion (e.g., a programmable amount), thereby causing emitter  1442  to emit a projectile  1412 . According to some examples, agricultural projectile delivery system  1430  may be adapted for non-agricultural uses, and may be used to deliver any type of projectile, including units of solids or gases, for any suitable application. 
       FIG. 15  is a diagram depicting an example of an arrangement of emitters oriented in one or more directions in space, according to some examples. Diagram  1500  includes an arrangement of emitters  1537 , one or more of which may be oriented at an  1538  relative to, for example, the ground. Alternatively, one or more subsets of emitters  1537  may be oriented in any angle in the Y-Z plane, any angle in the X-Z plane, or at any angle or vector in an X-Y-Z three-dimensional space. In some examples, one or more optical arrays aligned with emitters  1537  may intersect at one or more locations in space, at which an image capture device may be disposed. 
       FIG. 16  is a diagram depicting an example of another arrangement of emitters configured to be oriented in one or more directions in space, according to some examples. Diagram  1600  includes an arrangement of emitters  1637 , whereby one or more emitters may be configurable to adjust one or more orientations to implement agricultural projectile trajectories in any direction. In this example, a subset of emitters  1637  is depicted to include emitters  1639   a ,  1639   b ,  1639   c , and  1639   d  being oriented to, for example, select a trajectory that may optimally deliver a treatment to a target. In some implementations, orientations of each of emitters  1639   a ,  1639   b ,  1639   c , and  1639   d  may be configured to deliver at least one agricultural projectile  1612  in the presence of obstructive objects  1640 , such as a cluster of blossoms growing over and in front of (e.g., in between) a target agricultural object  1699 . As shown, obstructive objects  1640  obstruct trajectories  1690  associated with emitters  1639   a ,  1639   b , and  1639   c . Thus, an orientation and/or a position of emitter  1639   d  facilitates implementing an unobstructed trajectory over which to propel agricultural object  1612  to intercept target objects  1699 . 
     In some examples, emitters  1639   a ,  1639   b ,  1639   c , and  1639   d  may each be associated with a camera, such as one of cameras  1641   a ,  1641   b ,  1641   c , and  1641   d . Cameras  1641   a ,  1641   b ,  1641   c , and  1641   d  may be implemented to detect alignment (e.g., unobstructed alignment) with a target. Note that while diagram  1600  depicts cameras  1641   a ,  1641   b ,  1641   c , and  1641   d  adjacent to corresponding emitters, any of cameras  1641   a ,  1641   b ,  1641   c , and  1641   d  may be implemented as “in-line” cameras in which an emitter is disposed in a field of view. 
     According to various examples, emitters  1639   a ,  1639   b ,  1639   c , and  1639   d  may have configurable orientations that may be fixed during application of treatments. In other examples, one or more of emitters  1639   a ,  1639   b ,  1639   c , and  1639   d  may have programmable or modifiable orientations or trajectories. As shown, an alignment device  1638  may include logic and one or more motors to orient an emitter to align a trajectory in any direction in three-dimensional space. As such, alignment device  1638  may be configured to modify orientations of emitters in-situ (e.g., during application of treatments). 
       FIG. 17  is a diagram depicting one or more examples of calibrating one or more emitters of an agricultural projectile delivery system, according to some examples. Diagram  1700  includes an in-line camera  1701  and an agricultural projectile delivery system  1730  including any number of emitters, such as emitter  1713 , disposed in the field of view of in-line camera  1701 . Diagram  1700  also includes a target  1760  disposed on surface  1703  and calibration logic  1740 , which may include hardware and/or software to facilitate calibration of a trajectory of emitter  1713  to guide an emitted agricultural projectile  1712  via a calibrated trajectory to intercept target  1760 . In calibration mode, emitter  1713  may be identified or selected for calibration. In some examples, an agricultural projectile trajectory associated with emitter  1713 , such as uncalibrated trajectory  1719 , may be adjusted to align, for example, coaxially with an optical ray  1717 . In at least one alternative example, a timing of activation (e.g., a trigger or activation event at a point in time or within a time interval) may be calibrated to cause agricultural projectile  1712  to optimally intercept target  1760  within a range of accuracy and precision. In other examples, any other operational characteristic of either emitter  1713  or agricultural projectile delivery system  1730  may be calibrated, including, but not limited to, pressure, time-of-flight, rates of dispersal, windage (e.g., to compensate for airflow, whether vehicle-based or wind), etc. 
     Diagram  1700  also depicts an in-line targeting image  1720  generated by in-line camera  1701 . As a number of emitters are disposed in the field of view of in-line camera  1701 , a portion of optical ray  1717  may be an occluded portion  1717   a . In-line targeting image  1720 , which is a subset of image data, includes image data representing an emitter array  1730   a  that may occlude visibility to target  1760 . Also included is image data representing an optical sight image  1713   b . Calibration logic  1740  may be configured to access image data to calculate adjustment parameters. In some examples, calibration logic  1740  may be configured to compute an alignment (or associated calibration parameters) of one or more pixels associated with optical sight image  1713   b  to target  1760 , and through one or more points in space associated with an aperture of emitter  1712 . Hence, each of optical sight image  1713   b , emitter  1713 , and target  1760  may be calibrated to lie (or substantially lie) on an optical ray  1717 , thereby forming a calibrated trajectory. In some examples, any emitter may be calibrated to coaxially align with any optical ray that extends from any optical sight to a corresponding target. As such, one or more emitters may be calibrated within a two dimensional plane that may include optical rays extending from optical sight images (and pixels thereof) at different angles. 
     In a first calibration implementation, calibration logic  1740  may be configured to calculate or predict a projectile impact site  1762  at surface  1703  that may be relative to a reference of alignment. In at least some examples, a focused light source may be implemented to provide a reference alignment mark. In one implementation, a focused light source may project coherent light, such as generated by a laser  1715  (e.g., a laser pointer or other generator of a beam of laser light), as a reference mark onto surface  1703 . To calibrate emitter  1713 , a laser  1715  may be affixed in relation to uncalibrated trajectory  1719  of emitter  1713  so that emitted laser light terminates or impinges on surface  1703  (i.e., forms a reference mark) that coincides with a projectile impact site  1762  if projectile  1712  was propelled to impact surface  1703 . Hence, a point on surface  1703  at which coherent light impinges may be aligned with projectile impact site  1762 . In this configuration, a direction of emitted laser light and a direction of emitter  1713  may be varied in synchronicity to adjust a predicted impact site  1762  (i.e., reflected laser light) to coincide with target  1760 , which may be aligned with optical array. 
     In some examples, calibration logic  1740  may be configured to access in-line targeting image data  1720 , or any other image data, to receive image data depicting reflected laser light emanating from predicted projectile impact site  1762 . Calibration logic  1740  may be configured to calculate one or more calibration parameters to align predicted projectile impact site  1762  with optical ray  1717 . For example, calibration logic  1740  may calculate calibration parameters that include an elevation angle and/or an elevation distance  1761  (e.g., in a Y-Z plane) as well as an azimuthal angle and/or an azimuthal distance  1763  (e.g., in an X-Z plane). In at least one implementation, a direction of emission of emitter  1713  may be adjusted to align reflected laser light with optical ray  1717  by, for example, adjusting direction of an aperture of a nozzle. Therefore, predicted impact site  1762  may be adjusted by an elevation angle and an azimuthal angle to coincide with target  1760 . Note that adjusting projectile impact site image  1762   b  may cause it to become occluded in image  1720  as it is aligned with target image  1760   b.    
     In at least one case, to confirm calibration, a confirmatory agricultural projectile  1712  may be propelled to confirm sufficient calibration upon intercepting target  1760 . Calibration logic  1740  may be configured to detect impact of confirmatory agricultural projectile  1712  at target  1760 , and if adjustment may be available, then calibration logic  1740  may further compute calibration parameters. Target  1760  may be implemented at a horizontal distance from emitter  1713 , the horizontal distance being perpendicular or substantially perpendicular to a direction of gravity. 
     In a second calibration implementation, a visual fiducial marker (not shown) may be attached to a back of each emitter  1713 , and an alignment arm (not shown) may be coupled to each emitter such that an alignment arm may be configured to rotate a nozzle. The alignment arm may be manually or autonomously rotated to cause visual fiducial marker to become visible an image. When visible, calibration logic  1740  may deem emitter  1713  (e.g., a nozzle) aligned with optical ray  1717 . This implementation enables servoing to effectuate calibration using image  1720 , with optional use of target  1760 . 
     In yet another calibration implementation, one or more cameras, such as AR camera  1703 , may be implemented with in-line camera  1701  to calibrate an emitter trajectory, whereby AR camera and in-line camera  1701  may capture imagery in synchronicity. In this example, AR camera  1703  may be configured to facilitate imagery with augmented reality (“AR”). Hence, AR camera  1703  may be configured to generate a virtual target image  1762   a  for target  1760  in calibration target image  1722 . As shown, calibration target image  1722  includes a virtual target image  1762   a  that may include one or more image pixels  1770  that coincide with optical sight  1713   b . As shown, virtual target image  1762   a  is not occluded by an array of emitters. Therefore, projectile impact site image  1760   a , which may be identified by reflective laser light, may facilitate adjustment to align with virtual target image  1762   a . When aligned, optical sight  1713   b , aperture direction of emitter  1713 , and target  1760  may be aligned with optical ray  1717 . In this implementation, calibration target image  1722  omits occluded imagery associated with image  1720 . 
       FIG. 18  is a diagram depicting another one or more examples of calibrating one or more emitters of an agricultural projectile delivery system, according to some examples. Diagram  1800  includes an in-line camera  1801  and an agricultural projectile delivery system  1830  including any number of emitters, such as emitter  1813 , disposed in the field of view of in-line camera  1801 . Diagram  1800  also includes one or more light sources  1862  disposed on surface  1803 , such as light sources  1862 . In some examples, light sources  1862  may be reflective light (e.g., reflective laser light) originated at points  1863   a  and  1863   b  (lasers not shown). Diagram  1800  includes calibration logic  1840  that may include hardware and/or software configured to facilitate calibration of a trajectory  1815  of emitter  1813  to guide an emitted agricultural projectile via a calibrated trajectory to intercept a target (not shown). In calibration mode, emitter  1813  may be identified or selected for calibration. In some examples, an agricultural projectile trajectory  1815  associated with emitter  1813  may be adjusted to align, for example, coaxially with an optical ray  1817 . 
     In this example, emitter  1813  may be coupled (e.g., rigidly) to one or more boresights  1814 . As shown, boresight  1814   a  and boresight  1814   b  are affixed at a distance  1811  to emitter  1813 , and each boresight includes an interior space through which light may pass as a boresight is aligned with a source of light (or beam of light). In some examples, each interior space of boresight  1814   a  and boresight  1814   b  may be oriented coaxially with a line in three-dimensional space at any angle, regardless whether boresight  1814   a  and boresight  1814   b  are similarly or differently oriented. In this configuration, boresight  1814   a  and boresight  1814   b  are oriented such that when corresponding sources of light passes through each, emitter  1813  is deemed aligned with optical ray  1817 . For example, if beams of light  1818   a  and  1819   b , originating at respective sources of light  1862 , are detected to pass through boresight  1814   a  and boresight  1814   b , respectively, a trajectory for emitter  1813  is aligned with optical ray  1817 . Note that distance  1811  may be sufficient to enable beams of light  1818   a  and  1819   b  to pass through boresight  1814   a  and boresight  1814   b , respectively, and be detectable as aligned beam images  1818   b  and  1819   b , respectively, in in-line targeting image  1820 . 
     Diagram  1800  also depicts an in-line targeting image  1820  generated by in-line camera  1801 . As a number of emitters are disposed in the field of view of in-line camera  1801 , a portion of optical ray  1817  may be an occluded portion. In-line targeting image  1820 , which is a subset of image data, includes image data representing an emitter array  1830   a  that may occlude visibility to a target. Also included is image data representing an optical sight image  1813   b . Calibration logic  1840  may be configured to access image data, such as aligned beam images  1818   b  and  1819   b , to calculate adjustment parameters to align boresight  1814   a  and boresight  1814   b  to beams of light  1818   a  and  1819   b , thereby causing alignment of emitter  1813  coaxially to optical ray  1817 . Calibration logic  1840  may further be configured to detect aligned beam images  1818   b  and  1819   b  during calibration to indicate projectile trajectory  1815  is aligned. 
       FIG. 19  is an example of a flow diagram to calibrate one or more emitters, according to some embodiments. Flow  1900  begins at  1902 . At  1902 , a calibration mode is entered to calibrate one or more emitters during which a trajectory of an emitter (e.g., a nozzle) may be adjusted to intercept a target, such as an agricultural object. In calibration mode, hardware and/or software may be configured to implement calibration logic to facilitate calibration. In calibration mode, an emitter of an agricultural projectile delivery system may be identified or otherwise selected for calibration. For example, an emitter may be adjusted to calibrate a trajectory of an agricultural projectile to intercept a target. Hence, an uncalibrated trajectory may be adjusted to align, for example, coaxially with an optical ray, at least in some examples. 
     At  1904 , a determination is made as to whether multiple cameras may be used during calibration. For example, at least one additional camera may be used to generate augmented reality-based imagery. If no, flow  1900  continues to  1906 , at which focused light sources may be implemented to calibrate alignment. Examples of focused light sources include coherent light sources (e.g., laser light sources), or any other type of light source. At  1908 , a determination is made as to whether one or more boresights may be implemented. If not, flow  1900  continues to  1910 , at which a laser beam may be aligned with an emitter aperture (e.g., trajectory) direction to align to a reference mark (e.g., laser light) that may be coincident to a predicted projectile impact site (e.g., via the trajectory). A determination is made at  1912  as to whether a reference laser light coincides with a target, which may be aligned with an optical ray. If there is not a deviation, then an emitter trajectory may be deemed calibrated. But if there is a deviation, flow  1900  continues to  1914  at which one or more calibration parameters may be determined (e.g., elevation—related parameters, azimuthal-related parameters, and the like). At  1916 , emitter (e.g., nozzle) may be adjusted relative to a number of elevation degrees or azimuthal degrees, and flow  1900  continues to determine if another calibration adjustment results in calibration. 
     Referring back to  1908 , if a determination indicates a boresight is implemented, flow  1900  continues to  1930 . At  1930 , one or more boresights may be implemented with an emitter. In some cases, one or more boresights may be oriented with one or more lines, and may be rigidly affixed to an emitter. In other cases, one or more boresights need not be rigidly affixed and may be adjustably moveable relative to an emitter. At  1932 , one or more light sources may be identified for alignment, the light being reflective light from one or more lasers. At  1934 , an emitter may be adjusted manually or autonomously to align one or more boresights with one or more light sources. At  1936 , calibration may be evaluated by, for example, detecting light beams passing through each boresight. If each boresight is detected to allow light to pass through its interior, then an emitter is calibrated. Otherwise, flow  1900  continues back to  1934  to perform a next calibration operation. 
     Referring back to  1904 , if a determination indicates multiple camera may be used, flow  1900  continues to  1960 . At  1960 , an automated reality (“AR”)-aided calibration camera may be implemented. At  1962 , at least two cameras may be synchronized to capture images of a target or other objects in synchronicity. For example, an in-line camera and an AR camera may be synchronized such that, for example, each pixel in an image generated by the AR camera is similar or equivalent to a corresponding pixel in the in-line camera. At  1964 , a virtual target image may be generated in an image generated by an AR camera, the virtual target image including pixels associated with an optical sight in another image generated by an in-line camera. At  1966 , a laser beam aligned with a direction of an emitter may be generated. At  1968 , an emitter (or nozzle) may be adjusted to align an image of a laser beam with a virtual target image, thereby calibrating an emitter. At  1970 , calibration of an emitter may be evaluated, and may be recalibrated if so determined at  1972 . If recalibration is needed, flow  1900  returns to  1966 . Otherwise, flow  1900  terminates. 
       FIGS. 20 and 21  are diagrams depicting an example of calibrating trajectories of agricultural projectiles in-situ, according to some examples. Diagram  2000  of  FIG. 20 , which is a rear view of exemplary calibration components, includes an agricultural projectile delivery system  2001  including a motion estimator/localizer  2019  and one or more sensors  2070 , including airflow direction sensor  2027  and airflow speed sensor  2029 . Diagram  2000  also includes a windage emitter  2017  and an emitter  2013 , as well as image capture devices  2041   a  and  2041   b . Windage emitter  2017  may be configured to emit a sacrificial or test emission, such as projectile  2011   a , to determine, for example, effects of abiotic or environmental factors, including effects of a gust of wind on a trajectory of emitter  2013 . Emitter  2013  may be configured to deliver a treatment to an agricultural target  2022   a  at an emission time at which target  2022   a  aligns with a point at optical sight  2026   a . Note that elements depicted in diagram  2000  of  FIG. 20  may include structures and/or functions as similarly-named elements described in connection to one or more other drawings. 
     In operation, target  2022   a  may be identified as an agricultural object to which a treatment may be applied via emitter  2013 . Sensors  2070  may be used to identify a non-actionable target, such as a leaf  2023   a , to perform a windage evaluation. Hence, logic in agricultural projectile delivery system  2001  may classify leaf  2023   a  as a windage target  2023   a . In some examples, windage emitter  2017  may be configured to emit an inert material, such as water, as projectile  2011   a  to evaluate wind as a factor. Camera  2041   a  may be used to determine whether water-based projectile  2011   a  intercepts windage target  2023   a . Based on whether projectile  2011   a  intercepts windage target  2023   a , a trajectory for emitter  2013  may be modified in-situ (e.g., during application of one or more treatments) to enhance probabilities that an agricultural projectile  2012   a  intercepts target  2022   a . Camera  2041   b  may be used to determine whether projectile  2012   a  intercepts target  2022   a . Note that in some cases, windage emitter  2017  may be implemented as another emitter  2013  that applies a treatment. 
       FIG. 21  is a diagram  2100  that includes a top view of exemplary calibration components, whereby sensors  2027  and  2029  and cameras  2041   a  and  2041   b  may be implemented to adjust trajectories of emitter  2013  to counter environmental effects including wind. Note that elements depicted in diagram  2100  of  FIG. 21  may include structures and/or functions as similarly-named elements described in connection to  FIG. 20  or any other one or more other drawings. In operation, consider an example in which an original trajectory  2132  of a windage projectile  2111   b  may be generated by logic of agricultural projectile delivery system  2001  to counter wind as sensed by sensors  2027  and  2029 . Further, the logic may be configured to track a time to emit windage projectile  2111   b . As shown, camera  2014   a  may capture imagery depicting windage projectile  2111   b  being deflected onto a deflected trajectory  2133  due to, for example, wind or other external forces. In response, logic of agricultural projectile delivery system  2001  may be configured to adjust original trajectory  2137  of emitter  2013  to calculate an adjusted trajectory  2139  so that an agricultural projectile may be delivered to a target via a predicted trajectory  2136 . In some cases, adjusted trajectory  2139  may be associated with an adjusted activation time  2126   b  at which emitter  2013  may propel agricultural projectile  2112   b  via a predicted trajectory  2139  so as to intercept target  2022   a  as if aligned with optical sight  2026   a . Note that adjusted activation time  2126   b  may be adjusted from an initial activation time by an amount identified as differential activation time  2138 . In some alternative examples, payloads  2192  emitted by windage emitter  2017  and emitter  2013  may be associated with heating/cooling (“H/C”) elements  2190  to apply or extract different amounts of thermal energy. In examples in which cameras  2041   a  and  2041   b  are configured to detect infrared light, payloads  2192  may be elevated or cooled to different temperatures for application to agricultural objects at night time (e.g., without sunlight). Temperature differentials of payloads may be distinguishable from each other as well as from infrared light reflected from various agricultural objects. As such, one or more agricultural treatments may be applied to agricultural objects at any time of the day regardless of the presence of sunlight. 
       FIG. 22  is a diagram depicting deviations from one or more optical sights to another one or more optical sights, according to some examples. Diagram  2200  includes an image capture device  2204  configured to generate an agricultural environment image  2201   b  based on agricultural environment  2201   a . Diagram  2200  also includes an agricultural treatment delivery vehicle  2219  with a ground-mapping sonar/radar sensor unit  2270  and a trajectory processor  2283  of an agricultural treatment delivery system  1284 . At a time, t 1 , trajectory processor  2283  may be configured to detect a first subset of optical sights for images of targets  2222   a ,  2222   b , and  2222   c , and may be further configured to track movement of pixels representing images of targets  2222   a ,  2222   b , and  2222   c  via, for example, a projected alignment tracking line, such as line  2223 . Here, trajectory processor  2283  at time, t 1 , may be configured to select optical sight  2226   b  to align with target  2222   a , select optical sight  2226   c  to align with target  2222   b , and select optical sight  2226   d  to align with target  2222   c.    
     At time, t 2 , initial positions of an array of emitters  2291  at time, t 1 , is changed to another position (e.g., relative to positions of targets  2222   a  to  2222   c ) and is depicted as an array of emitters  2292  at time, t 2 . For example, one or more sensors (e.g., accelerometers and the like) may detect a change in elevation  2239  as vehicle  2210  traverses uneven soil topology  2290 . Responsive to a change in elevation, a second subset of optical sights may be selected to align with targets  2222   a  to  2222   c . For example, optical sight  2227   a  may be selected to align with target  2222   a , optical sight  2227   b  may be selected to align with target  2222   b , and optical sight  2227   c  may be selected to align with target  2222   c . Therefore, trajectory processor  2283  may be configured to de-select and select any optical sight as a function of whether an optical sight is optimal. Ground-mapping sonar/radar sensor unit  2270  may be configured to scan a surface of ground topology  2290  to identify elevations (e.g., bumps) to predict changes in optical sight selection. 
       FIG. 23  is a diagram depicting an agricultural projectile delivery system configured to implement one or more payload sources to provide multiple treatments to one or more agricultural objects, according to some examples. Diagram  2300  includes an image capture device  2304  configured to capture an agricultural environment image  2320  of an agricultural environment  2301 , which may include any number of plants (e.g., trees), at least in the example shown. Diagram  2300  also includes an agricultural projectile delivery system  2381  configured to identify multiple types of agricultural objects via image  2320  in agricultural environment  2301 , select an action associated with at least a subset of different types of agricultural objects, and deliver a specific treatment to a subset of agricultural objects as a function of, for example, a type or classification of agricultural object, as well as other factors, including context (e.g., season, stage of growth, etc.). Agricultural projectile delivery system  2381  may be configured to receive policy data  2372 , indexed object data  2374 , sensor data  2376 , and position data  2378 , one or more of which may be implemented as described herein. Further, agricultural projectile delivery system  2381  may be configured to select one or more payload sources  2390  (and amounts thereof) to apply as multiple agricultural projectiles either sequentially or simultaneously. For example, agricultural projectile delivery system  2381  may be configured to select different payload sources  2390  to deliver different agricultural projectiles, such as agricultural projectiles  2312   a  to  2312   j . As shown, agricultural projectile delivery system  2381  may include a target acquisition processor  2382 , a trajectory processor  2383 , and an emitter propulsion subsystem  2385 , one or more of which may have one or more functionalities and/or structures as described herein. Note that elements depicted in diagram  2300  of  FIG. 23  may include structures and/or functions as similarly-named elements described in connection to one or more other drawings. 
     In at least one example, target acquisition processor  2382  may include an object identifier  2384 , an event detector  2386 , an action selector  2388  and an emitter selector  2387 . In some implementations, functions and structures of emitter selector  2387  may be disposed in trajectory processor  2383 . Object identifier  2384  may be configured to identify and/or classify agricultural objects detected in agricultural environment image  2320  based on, for example, index object data  2374  and sensor data  2376 , among other data. To illustrate operation of object identifier  2384 , consider that object identifier  2384  may be configured to detect one or more objects  2322   a  to  2329   a , each of which may be classifiable. For example, object identifier  2384  may detect and classify object  2322   a  as a blossom  2322   b . Object identifier  2384  may detect and classify object  2321   a  as a spur  2321   b , and may detect and classify object  2323   a  as a spur  2323   b . Object identifier  2384  may detect and classify object  2324   a  as a weed  2324   b , and may detect and classify object  2325   a  as a rodent  2325   b . Object identifier  2384  may detect and classify object  2326   a  as a disease  2326   b , such as a fungus. Object identifier  2384  may detect and classify object  2327   a  as a pest  2327   b , such as a wooly aphid. Object identifier  2384  may detect and classify object  2328   a  as a fruit  2328   b  to be applied with an identifying liquid that operates similar to a biological “bar code” to identity provenance. And, object identifier  2384  may detect and classify object  2329   a  as a leaf  2329   b.    
     One or more of event detector  2386  and action selector  2388  may be configured to operate responsive to policy data  2372 . Event detector  2386  may be configured to identify an event associated with one or more objects  2322   a  to  2329   a . For example, event detector  2386  may be configured to detect an event for blossom  2322   b , whereby associated event data may indicate blossom  2322   b  is a “king blossom.” Responsive to an event identifying a king blossom, action selector  2388  may be configured to determine an action (e.g., based on policy data  2372 ), such as applying a treatment that pollinates blossom  2322   b . As another example, consider that event detector  2386  may be configured to detect an event for weed  2324   b , whereby associated event data may indicate that weed  2324   b  has sufficient foliage prior to germination to be optimally treated with an herbicide. Responsive to generation of data specifying that an event identifies a growth stage of a weed, action selector  2388  may be configured to determine an action (e.g., based on policy data  2372 ), such as applying a treatment that applies an herbicide to weed  2324   b . Event detector  2386  and action selector  2388  may be configured to operate similarly for any identified agricultural object. 
     Emitter selector  2387  is configured to identify one or more optical sights for each subset of a class of agricultural objects (e.g., one or more optical sights may be associated with agricultural objects identified as pests  2327   b ). In various examples, one or more groups of optical sights may be used to treat multiple types or classes of agricultural objects. Trajectory processor  2383  may be configured to identify each subset of optical sights configured for a type or class of agricultural object and may track identified/classified agricultural objects as they move to corresponding optical sights. Upon detecting alignment of a type or class of agricultural object as a target with an optical sight, emitter propulsion subsystem  2385  may be configured to select one of payload sources  2390  to apply a specific treatment for a type or class of target that aligns with an associated optical sight. 
     In various examples, agricultural projectile delivery system  2381  may deliver customized treatments as agricultural projectiles to one or more types or classes of agricultural objects  2322   a  to  2329   a , any treatment may be performed individually and sequentially, or in combination of subsets thereof. To apply a treatment to blossom  2322   b , one or more agricultural projectiles  2312   a  originating from one of payload sources  2390  may be applied to blossom  2322   b . To apply a treatment to spur  2321   b , one or more agricultural projectiles  2312   b  originating from one of payload sources  2390  may be applied to spur  2321   b  to encourage growth (e.g., one or more agricultural projectiles  2312   b  may include a growth hormone). To apply a treatment to spur  2323   b , one or more agricultural projectiles  2312   c  originating from one of payload sources  2390  may be applied to spur  2323   b  to regulate growth (e.g., one or more agricultural projectiles  2312   c  may include a growth regulator to implement, for example, chemical pruning). To apply a treatment to weed  2324   b , one or more agricultural projectiles  2312   d  originating from one of payload sources  2390  may be applied to weed  2324   b  to terminate growth (e.g., one or more agricultural projectiles  2312   d  may include an herbicide). 
     To apply a treatment to rodent  2325   b , one or more agricultural projectiles  2312   e  originating from one of payload sources  2390  may be applied to rodent  2325   b  to reduce rodent population (e.g., one or more agricultural projectiles  2312   e  may include a rodenticide to disperse rodents, including voles, etc.). To apply a treatment to disease  2326   b , one or more agricultural projectiles  2312   f  originating from one of payload sources  2390  may be applied to disease  2326   b  to reduce a disease (e.g., one or more agricultural projectiles  2312   g  may include a fungicide to reduce, for example, apple scab fungi). To apply a treatment to pest  2327   b , one or more agricultural projectiles  2312   g  originating from one of payload sources  2390  may be applied to pest  2327   b  to reduce an infestation of an insect (e.g., one or more agricultural projectiles  2312   g  may include an insecticide to reduce, for example, wooly aphid populations). To apply a treatment to leaf  2329   b , one or more agricultural projectiles  2312   h  originating from one of payload sources  2390  may be applied to leaf  2329   b  to apply a foliage fertilizer or reduce leaf-related diseases (e.g., one or more agricultural projectiles  2312   h  may include a fungicide to reduce, for example, peach leaf curl for peach trees). To apply a treatment to fruit  2328   b , one or more agricultural projectiles  2312   j  originating from one of payload sources  2390  may be applied to fruit  2328   b  to apply a biological or molecular-based tag (e.g., one or more agricultural projectiles  2312   j  may include a synthetic DNA to apply to a crop to identify provenance at various degrees of resolution, such as from a portion of an orchard to a tree to an agricultural object.). 
     According to some examples, payload sources  2390  each may be contained a vessel that may be configured as a “cartridge,” which may be adapted for efficient connection and re-filling over multiple uses in contest of employing autonomous agricultural treatment delivery vehicles as, for example, a “robotic-agricultural-vehicles-as-a-service.” In some examples, payload sources  2390  may include any type or amount of chemistries, any of which may be mixed together in-situ (e.g., during application of treatments), whereby logic in agricultural projectile delivery system  2381  may be configured to determine ratios, proportions, and components of mixtures, whereby any one of agricultural projectiles  2312   a  to  2312   j  may be composed of a mixture of chemistries (e.g., derived from two or more payload sources  2390 ). Mixture of the chemistries may occur as an agricultural treatment delivery vehicle traverses paths when applying treatments. As such, mixing of chemistries in real-time (or near real-time) provides for “just-in-time” chemistries for application to one or more agricultural objects. In some cases, “recipes” for mixing chemistries may be received and update in real time as a vehicle is traversing paths of an orchard. According to some examples, payload sources  2390 , as cartridges, may be configured to apply an agricultural projectile as an experimental treatment. As such, application of an experimental agricultural projectile may include applying an experimental treatment to agricultural objects to implement a test including A/B testing or any other testing technique to determine an efficacy of a treatment. 
       FIG. 24  is an example of a flow diagram to implement one or more subsets of emitters to deliver multiple treatments to multiple subsets of agricultural objects, according to some embodiments. Flow  2400  begins at  2402 . At  2402 , sensor-based data describing an environment may be received, the sensor-based data representing agricultural objects for a geographic boundary. For example, as an agricultural projectile delivery vehicle traverses one or more paths to deliver multiple treatments to multiple subsets of agricultural objects, sensor data (e.g., image data) may also be captured for later analysis or to facilitate delivery one or more treatments to one or more agricultural objects. 
     At  2404 , data representing one or more subsets of indexed agricultural objects may be received. For example, each subset of indexed agricultural objects may relate to a different type or class of agricultural object. One subset of indexed agricultural objects may relate to a class or type of fruit disease, whereas another subset of indexed agricultural objects may relate to a class or type of pest. Another subset of indexed objects may relate to a class or type of stage-of-growth of, for example, a fruit bud. 
     At  2406 , data representing one or more policies may be received. At least one policy may be received in association with a subset of indexed agricultural objects, whereby at least one policy may specify one or more actions or treatments to be performed for a class or type of agricultural objects. 
     At  2408 , each agricultural object in a subset of agricultural objects may be identified. For example, indexed agricultural object data may include identifier data that uniquely relates to a unique agricultural object, such as a one cluster of apple buds, whereby the cluster of apple buds may be distinguishable for any other cluster on the tree, or throughout an orchard, or other geographic boundary. Or, a determination to apply a treatment to an agricultural object may be determined in-situ absent policy information for a particular agricultural object. For example, an agricultural object may be changed state, which had been undetected or unpredicted. Agricultural projectile delivery vehicle may detect the changed state in real time and apply a treatment absent a policy for that object. 
     At  2410 , an action to be applied may be selected, based on policy data, the action being linked to the agricultural object. At  2412 , an emitter may be selected to apply the action. For example, an emitter may be configured to deliver one or more units of treatment (e.g., one or more agricultural projectiles) to an agricultural object. At  2414 , a determination is made whether there is another agricultural object in a subset or class of agricultural objects. If so, flow  2400  moves back to  2410 . Otherwise, flow  2400  moves to  2416  at which a determination is made as to whether there is another subset of agricultural objects for which a treatment may be applied. If so, flow  2400  moves back to  2410 , otherwise flow  2400  moves to  2418 . At  2418 , various subsets of emitters may be activated to provide treatment to multiple sets of agricultural objects, for example, as an agricultural treatment delivery vehicle traverses over one or more paths adjacent to the multiple subsets of agricultural objects. 
       FIG. 25  is an example of a flow diagram to implement one or more cartridges as payload sources to deliver multiple treatments to multiple subsets of agricultural objects, according to some embodiments. According to various examples, an agricultural treatment delivery system may granularly, with micro-precision, monitor agricultural objects over time (e.g., through stages-of-growth), whereby an agricultural object may be a basic unit or feature of a tree (e.g., a leaf, a blossom, a bud, a limb, etc.) 
     that may be treated with micro-precision rather than, for example, spraying a plant as a whole. Therefore, implementation of one or more agricultural treatment delivery vehicles, which may operate autonomously to navigate and apply agricultural treatments, may conserve amounts of chemistries (e.g., amounts of fertilizers, herbicides, insecticides, fungicides, growth hormones, etc.). Further, as agricultural treatment delivery systems may be implemented in a fleet of autonomous agricultural treatment delivery vehicles, an entity that provides “robotic-agricultural-vehicles-as-a-service” may be able to access and use a variety of chemistries from a variety of manufacturers, including relatively expensive chemistries under research and development that are often out of the reach of small and impoverished farmers. As such, an entity can distribute costs over a broad user base, thereby enabling smaller farmers and impoverished farmers to access chemistries they might otherwise may not have access via use of an agricultural treatment delivery vehicle as described herein. 
     Normatively, agricultural chemicals are available for purchase predominantly in units of 275 gallons (e.g., in a tote container), 5 gallon buckets, or 2.5 gallon jugs, among others. Cost savings by buying in bulk may be less cost effective if amounts remain used. According to various examples, autonomous agricultural treatment delivery vehicles described herein may implement “cartridges” as payload sources that facilitate ease of replacement or refilling (e.g., in-situ). For example, an autonomous agricultural treatment delivery vehicles may autonomously detect insufficient amounts of a chemistry (e.g., based on policy data that requires an action to consume that chemistry), and then may autonomously refill its payload at refilling stations located on a farm or remotely. Or, cartridges may be shipped to a destination to replace empty or near-empty cartridges. 
     In view of the foregoing, flow  2500  begins at  2502 . At  2502 , action data may be received, for example, to perform one or more policies. For example, action data may be received from a precision agricultural management platform configured to employ computational resources to analyze previously-recorded sensor data from autonomous agricultural treatment delivery vehicles for purposes of generating policies with which to treat numerous agricultural objects in a geographic boundary, such as in an orchard. At  2504 , a determination is made as to whether action data is received into an autonomous agricultural treatment delivery vehicle or an agricultural projectile delivery system (e.g., with manual navigation) for performing one or more policies associated with an orchard or a farm. If yes, flow  2500  moves to  2511 . At  2511 , action data may be stored in on-board memory as policy data, which may be configured to specify specifics treatments that are to be applied to specific agricultural objects, at particular times and/or amounts, or in accordance with any other parameter. 
     At  2513 , a target acquisition processor may be configured to apply one or more actions for a subset agricultural objects. For example, the target acquisition processor may be configured to identify and enumerate each agricultural object that is identified as receiving particular action, and thus may determine an amount of payload that is to be distributed over a number of agricultural projectiles to treat a number of agricultural objects. At  2515 , computations are performed to determine whether payload sources (e.g., in cartridges) are sufficient to implement actions over a group of identified agricultural objects. At  2517 , a determination is made as to whether payload source amount is insufficient. If not, at least one cartridge may need to be charged (e.g., filled to any level) or replaced at  2519 . For example, an autonomous agricultural treatment delivery vehicle may drive autonomously to a refilling station local to, for example, an orchard or farm. As such, a cartridge may be charged with one of a germination payload (e.g., pollen) or a cluster-thinning payload (e.g., ATS/Lime Sulfur, or the like). Or, one or more cartridges may be shipped to that location. Regardless, flow  2500  moves to  2531  to optionally generate one or more maps to navigate at least one or more emitters to apply one or more actions associated with the group of identified agricultural objects. At  2533 , an autonomous agricultural treatment delivery vehicle may be navigated in accordance with the map. At  2535 , one or more emitters may be configured to execute the actions to, for example, deliver treatments to one or more targeted agricultural objects. 
     Referring back to  2504 , if action data is not received into an autonomous agricultural treatment delivery vehicle or an agricultural projectile delivery system, then flow  2500  continues to  2506 . At  2506 , a computing device is identified that may be configured to provision one or more cartridges to include one or more payload sources. For example, the computer device may be implemented at a geographic location at which cartridges may be provisioned at distances relatively close to a geographic boundary, such as a farm or an orchard. At  2508 , a subset of cartridges may be provisioned as a function of one or more policies with which to implement the action data. At  2510 , a determination is made as to whether a policy ought to be updated. For example, recently received sensor data may indicate, for example, a sufficient number of crops have entered a later stage of growth, which may cause flow  2500  to move to  2522  to select payloads types customized to accommodate modifications in policies (e.g., changes in payload types to be applied to agricultural objects). At  2524 , one or more cartridges may be filled, for example, remotely from a geographic boundary (e.g., an orchard) and shipped to a destination via a package-delivering service or via an entity providing an autonomous agricultural treatment delivery vehicle as a service. At  2526 , one or more cartridges may be transported to the geographical boundary for which policy data applies. At  2528 , action data (i.e., policy data) may be transmitted to an agricultural projectile delivery system for implementation along with cartridges shipped to a location at which the agricultural projectile delivery system is located. 
       FIGS. 26 to 31  are diagrams depicting components of an agricultural treatment delivery vehicle configured to sense, monitor, analyze, and treat one or more agricultural objects of a fruit tree through one or more stages of growth, according to some examples.  FIG. 26  includes one or more components of an agricultural treatment delivery vehicle  2601 , including various vehicle components  2610  (e.g., drivetrain, steering mechanisms, etc.), a mobility platform  2614 , a sensor platform  2613 , one or more payload sources  2612 , and an agricultural projectile delivery system  2611 . Agricultural treatment delivery vehicle  2601  may be configured to identify one or more stages of growth for an agricultural object, and may be further configured to determine policies describing one or more actions or treatments to apply to various agricultural objects all year round, including a life cycle of a fruit crop from bud to harvest. Note that elements depicted in diagrams  2600  of  FIG. 26  through diagram  3100  of  FIG. 31  may include structures and/or functions as similarly-named elements described in connection to one or more other drawings or as otherwise described herein. Note, too, while  FIGS. 26 to 31  may refer to stages of growth for an apple crop, any one or more of the functions described herein may be applicable to other fruit trees, nut trees, or any other vegetation or plant, including vegetable crops (e.g., row crops, ground crops, etc.) and ornamental plants. 
     In some examples, one or more policies may include various actions to provide various treatments to agricultural objects depicted in diagrams  2600  to  3100 . For example, one or more policies may include data configured to manage apple crops with an aim to “save the king” (i.e., save a king bloom). One or more policies may be implemented over one or more stages of growth of an apple-related agricultural object. Agricultural projectile delivery system  2611  may be configured to apply one or more treatments to an agricultural object, such as a bud or blossom, with micro-precision by, for example, delivering a treatment as an agricultural projectile. Thus, agricultural projectile delivery system  2611  may treat portions of an apple tree on at least a per-cluster basis as well as a per-blossom basis, according to various examples. One or more policies may be configured to perform an action to isolate a king blossom on each cluster, and to perform another action to track one or more clusters (e.g., at an open cluster stage of growth) to detect, via sensor platform  2613 , whether a king blossom (as an agricultural object) has “popped.” Also, a policy may also track whether any lateral blossoms (as agricultural objects) have remained closed. Another policy may include performing an action to germinate a king blossom and to terminate neighboring lateral blossoms of a common cluster. Thus, lateral blossom may be autonomously terminated rather than being mechanically (e.g., manually by hand) terminated. In various examples, one or more policies configured to “save the king” may facilitate enhanced crop yields. For example, performing actions and treatments with micro-precision facilitates optimizing attributes of an apple, such as color, size, etc. Thus, agricultural projectile delivery system  2611  may assist in managing apple crops with micro-precision to enhance yields of apples that are sized optimally, for example, for packing. In some examples, about 88 apples per box may be obtained (e.g., rather than 100 apples per box). Also, terminating lateral blossom in accordance with functions and/or structures described herein facilitates increased amount of nutrients a fruit tree may supply to remaining blossoms to help produce larger, healthier fruit. A few policies may be implement to “thin a cluster,” thereby terminating each bud or blossom associated with a particular cluster. Hence, the various functions and/or structures described herein may enhance fruit production while reducing costs of labor. 
     Diagram  2600  depicts a portion of a limb, for example, in late winter or early spring (e.g., in the northern hemisphere) during a “dormant” stage of growth. As shown, the limb may include fruit buds  2621   a , leaf buds  2621   c , one or more spurs  2621   b , and one or more shoots  2621   d . Also shown is a fruit bud  2622  in a dormant state. One or more policies may cause agricultural projectile delivery system  2611  to inspect one or more portions of a limb to determine whether a treatment may be applied. For example, a foliar growth hormone may be applied as one or more agricultural projectiles to a spur to encourage growth. An example of a foliar growth hormone includes gibberellic acid, or gibberellin, or the like. By contrast, one or more policies may cause agricultural projectile delivery system  2611  to inspect a portion of a limb to determine whether “chemical pruning” may be implemented by applying a growth regulator (e.g., paclobutrazol or the like) as one or more agricultural projectiles. 
     Diagram  2700  depicts one or more buds  2722  (as agricultural objects) transitioning to a next stage of growth, such as a “half-inch” green  2724  (as an agricultural object). One or more policies may be configured to direct agricultural projectile delivery system  2611  to inspect and track development of buds  2722  and “half-inch” greens  2724 , and, if available, apply a treatment. 
     Diagram  2800  depicts one or more “half-inch” greens for  FIG. 27  transitioning to next stages of growth, such as a “tight cluster”  2826  (as an agricultural object), or a “full/pink cluster  2827  (as an agricultural object). Full/pink cluster  2827  may also be referred to as an open cluster. One or more policies may be configured to cause agricultural projectile delivery system  2611  to inspect and track development of agricultural objects  2826  and  2827 , and, if available, apply a treatment. For example, a determination may be made that tight cluster  2826  may be growing slower than as expected. As such, a policy may cause agricultural projectile delivery system  2611  to apply a growth hormone, with micro-precision, to tight cluster  2826  to promote growth. 
     Diagram  2900  depicts one or more agricultural objects  2826  and  2827  of  FIG. 28  transitioning to a next stage of growth. For example, a full/pink cluster  2927  may transition into a “king blossom” stage in which a king blossom  2928  (e.g., a first blossom) opens. In various examples, agricultural projectile delivery system  2611  may be configured to apply a treatment with micro-precision to center  2928   b  within a perimeter  2928   a  of king blossom  2928 . For example, a policy may cause agricultural projectile delivery system  2611  to apply a treatment to center  2928   b  by, for example, emitting an agricultural projectile to intercept center  2928   b  to germinate the blossom. In some cases, two or more king blossoms may be germinated and saved. 
     Diagram  3000  depicts one or more agricultural objects  2928  of  FIG. 29  transitioning to a next stage of growth. For example, a king blossom  2928  may transition into a “lateral” stage in which lateral blossoms  3029  open about king blossom  3028 . In various examples, agricultural projectile delivery system  2611  may be configured to apply a treatment with micro-precision to lateral blossoms  3029  (e.g., to the centers or portions thereof). For example, a policy may cause agricultural projectile delivery system  2611  to apply a treatment (e.g., a caustic chemical) to lateral blossoms  3029  to terminate growth of the lateral blossoms, thereby “saving the king.” Alternatively, some policies may cause agricultural projectile delivery system  2611  to apply a caustic treatment to both lateral blossoms  3029  and a king blossom  3028 , thereby thinning an entire cluster. 
     Diagram  3100  depicts one or more agricultural objects  3028  and  3029  of  FIG. 30  transitioning to next stages of growth. For example, lateral blossoms  3029  and king blossom  3028  may transition into a “fruit set” or “pedal fall” stage (as an agricultural object) in which petal have fallen. Also, king blossom  3028  of  FIG. 30  may transition to a “fruit” stage of growth in which a fruit  3130  develops and ripens. Agricultural projectile delivery system  2611  may emit an agricultural projectile to apply a synthetic DNA to fruit  3130  to identify its origins later in the food production process. Note that other policies, such as applying herbicides, insecticides, fungicides, and the like, may be implemented at one or more of the stages of growth described in  FIGS. 26 to 31 . 
       FIG. 32  is a diagram depicting an example of a flow to manage stages of growth of a crop, according to some examples. Flow  3200  starts at  3202 . At  3202 , an agricultural projectile delivery system may navigate autonomously to inspect, monitor, and treat one or more agricultural objects. At  3204 , sensors may be implemented to capture data representing agricultural objects. At  3205 , a predictive state of an agricultural object may be predicted, such as at a precision agricultural management platform, according to some examples. At  3206 , policy data configured to perform one or more actions for one or more agricultural objects may be accessed. At  3208 , a determination is made as to whether an action is associated with a pre-blossom stage. If so, flow  3200  moves to  3221  to apply a first subset of actions, such as applying growth hormone to a spur to promote limb growth. Otherwise, flow  3200  moves to  3210 , at which a determination is made as to whether an associated action relates to a blossom stage. If not, flow  3200  moves to  3232 . But if so, flow  3200  moves to  3223  to identify a blossom as “king” of a cluster. At  3225 , a second subset of actions may be performed, including germinating a blossom. Flow  3200  then moves to  3227 , at which a determination is made as to whether lateral blossoms are identified. If so, flow  3200  moves to  3229  to perform another action in the second subset of action, such as killing the lateral blossoms. Flow  3200  then moves to  3232 , at which a determination is made as to whether an action applies to a post-blossom stage. If so, flow  3200  moves to  3241  to perform a third subset of actions, such as applying a fungicide to a fruit exhibiting “apple scab.” Flow  3200  then moves  3234 , at which a determination is made as to whether a harvest is complete. If not, flow  3200  moves to  3202 , otherwise flow  3200  terminates. One or more of the above regarding flow  3200  may be implemented using an agricultural projectile delivery system, which may include one or more processors and one or more applications or executable instructions stored in memory. 
       FIG. 33  is a diagram depicting an agricultural projectile delivery vehicle implementing an obscurant emitter, according to some examples. Diagram  3300  includes an agricultural projectile delivery vehicle  3310  including an imaging device  3312  (e.g., a camera) and an optional illumination device  3314 , which may be omitted. Further, agricultural projectile delivery vehicle  3310  also may include an obscurant emitter  3321   c  that is configured to generate an obscurant wall  3320  interposed between, for example, a source of backlight, such as sun  3302 , and an agricultural object  3399 . As agricultural projectile delivery vehicle  3310  traverses a path adjacent a crop, such as a fruited tree, data generated by imaging sensors may be degraded when trying to capture an image of object  3399  that is disposed in between a bright source of backlight, such as sun  3302 , and camera  3312 . Generation of obscurant wall  3320  may facilitate an increase of a dynamic range for image capture device  3312  (e.g., enhancing a ratio between a largest value and a smallest value of luminous intensity). Obscurant wall  3320  forms a dynamic (e.g., temporary) enclosure as a light barrier to reduce an amount of light from backlight source  3302  relative to the agricultural object  3399 . In some examples, obscurant emitter  3321   c  may be implemented as a mist generator configured to generate portions  3332  and/or  3334  of mist or water vapor using, for example, an ultrasonic generator or transducers. Ultrasonic transducers may be configured to convert liquid water into a mist of the one or more clouds of water droplets, which may be viewed as “eco-friendly.” 
     In operation, a light intensity sensor (not shown) may be configured to detect a value of luminous intensity originating, for example, from backlight source  3302 . If a value of luminous intensity exceeds a threshold value, obscurant emitter  3321   c  may be configured to generate obscurant wall  3320  in, for example, a region between a medial line  3305  and backlight source  3302 . The obscurant may be directed that region using, for example, one or more blowers  3322   c  or directional fans. Portions of mist or fog provides a dynamic enclosure that may not have drawbacks of physical enclosures, such a shrouds, that are typically adapted for a specific row crop. Such a dynamic enclosure may adapt to differently-sized trees or crops. Further, by reducing backlight, obscurant wall may obviate a need to synchronize a camera sensor (e.g., a camera shutter synchronized with a flash) or perform additional image processing involving, for example, using multiple exposures or tone mapping algorithms. In some examples, one or more obscurant emitters  3321   a  and  3321   b  and one or more blowers  3322   a  and  3322   b  may be disposed on an encompassing structure  3325 , which may be omitted. 
       FIG. 34  is a diagram depicting an example of a flow to facilitate imaging a crop in an environment with backlight, according to some examples. In some examples, flow  3400  enhances a dynamic range of captured images of agricultural objects with environments with backlight, such as sunlight or moonlight (e.g., a full moon). At  3402 , location and/or sensor data of an environment including an agricultural object may be received. At  3404 , an agricultural object may be identified based on the location and/or sensor data received at  3402 . At  3406 , a determination is made as to whether an identified agricultural object may be correlated to index data. If so, flow  3400  moves to  3408 , at which a spatial location of an agricultural object may be determined. At  3410 , an identified agricultural object may be correlated to index object data, thereby confirming that sensor data (e.g., image data) being received at a sensor is identifying an agricultural object that is the same in the indexed data. At  3412 , an action may be associated with the indexed object data. That is, a policy to perform an action (e.g., a treatment) may be associated with indexed object data. At  3414 , an agricultural object may be identified as a target to, for example, perform an action. At  3416 , determination is made as to whether a value of backlight as above a threshold value. For example, if an intensity of light is above a threshold value, and that light originates behind the identified target, then flow  3400  moves to  3418  to generate an obscurant, such as generating water vapor using ultrasonic generator. At  3420 , an obscurant may be emitted at a location relative to the targeted agricultural object, the obscurant being disposed between a source of backlight and a target agricultural object. At  3422 , an image of the agricultural object may be captured. 
       FIG. 35  is a diagram depicting a pixel projectile delivery system configured to replicate an image on a surface using pixel projectiles, according to some embodiments. Diagram  3500  includes a pixel projectile delivery system  3511 , which may include any number of pixel emitters  3542   a , and a mobile computing device  3590 , which may include a processor configured to execute an application that may provide inputs (e.g., control data) to pixel projectile delivery system  3511 . In various examples, pixel projectile delivery system  3511  may be configured to emit subsets of pixel projectiles  3512  to “paint” or replicate portions of an image, such as image  3560 , upon a surface  3502 . In some examples, an application executing on mobile computing device  3590  may identify an image  3560  to be replicated on surface  3502 , and may further be configured to determine a reference with which to align inputs associated with mobile computing device  3590  and corresponding outputs associated with a replicated image on surface  3502 . As shown, a reference  3515   a  of image  3560  is aligned with reference  3515   b  of the image in the user interface of mobile computing device  3590 , which, in turn, may establish a reference  3515   c  associated with surface  3502 . Therefore, inputs into the user interface of mobile computing device  3590  may be correlated to reference  3515   b , and, similarly, outputs emitted out of emitters  3542   a  (and impacted points on surface  3502 ) may be correlated to reference  3515   c.    
     To illustrate operation of pixel projectile delivery system  3511 , consider that pixel projectile delivery system  3511  may be configured to receive data  3578  representing image  3560 . At least one portion  3515   a  of image  3560  may be a reference  3515   a  to align with a surface reference  3515   c  associated with surface  3502 . Pixel projectile delivery system  3511  may be configured to establish electronic communication with mobile computing device  3590 , which may be configured to transmit control data  3578  as a function of one or more spatial translations as inputs, whereby one or more spatial translations simulate replication on surface  3502 . Examples of one or more spatial translations are depicted as spatial transitions  3520   a ,  3521   a ,  3522   a ,  3523   a , and  3524   a . In some cases, each of spatial transitions  3520   a ,  3521   a ,  3522   a ,  3523   a , and  3524   a  may be referred to as a unit of spatial translation (e.g., a unit be determined by, for example, a momentary pause or delay in applying an input). 
     Pixel projectile delivery system  3511  may be configured to receive data representing a unit of spatial translation, such as one of units of spatial translation  3520   a ,  3521   a ,  3522   a ,  3523   a , and  3524   a , whereby the unit of spatial translation may specify a translation relative to a reference associated with mobile computing device  3590 . In one example, spatial translations may be determined based on translations of, for example, a simulated targeting sight  3592  that may produce each of spatial transitions  3520   a ,  3521   a ,  3522   a ,  3523   a , and  3524   a  relative to reference  3515   b , the translations of simulated targeting sight  3592  being caused by input into a touch-sensitive graphics user interface. In another example, spatial translations may be determined based on translations of, for example, motion in two-dimensional space that may produce each of spatial transitions  3520   a ,  3521   a ,  3522   a ,  3523   a , and  3524   a  relative to reference  3515   d . Thus, moving mobile computing device  3590  (e.g., within an X-Y plane) may produce spatial translations  3520   a ,  3521   a ,  3522   a ,  3523   a , and  3524   a  relative to reference  3515   d , whereby one or more motion sensors or accelerometers in mobile computing device  3590  generates inputs representing the spatial translations sent via control data  3578 . 
     Further, pixel projectile delivery system  3511  may be configured to determine one or more portions  3520   c ,  3521   c ,  3522   c ,  3523   c ,  3524   c  of image  3560  respectively associated with each unit of spatial translation  3520   b ,  3521   b ,  3522   b ,  3523   b , and  3524   b  relative to reference  3515   c . Note that pixel projectile delivery system  3511  may be configured to respectively map spatial transitions  3520   a ,  3521   a ,  3522   a ,  3523   a , and  3524   a  relative to reference  3515   d  to spatial translation  3520   b ,  3521   b ,  3522   b ,  3523   b , and  3524   b  relative to reference  3515   c.    
     Pixel projectile delivery system  3511  may be configured to identify one or more subsets of pixels (e.g., one or more portions  3520   c ,  3521   c ,  3522   c ,  3523   c ,  3524   c  of image  3560 ) to be formed on surface  3502  responsive to detecting a unit of spatial translation. And, pixel projectile delivery system  3511  may be configured to cause emission of one or more subsets of pixel projectiles  3512  directed to impact one or more portions of surface to form one or more subset of pixels  3520   d ,  3521   d ,  3522   d ,  3523   d , and  3524   d  relative to surface reference  3515   c  to form a replica  3550   b  of a portion  3550   a  of image  3560 . 
       FIG. 36  is a diagram depicting an example of a pixel projectile delivery system, according to some examples. Pixel projectile delivery system  3611  may include a target acquisition processor  3682 , which may include an object identifier  3684 . Pixel projectile delivery system  3611  may also include an emitter selector  3687 , a trajectory processor  3683 , and an emitter propulsion subsystem  3685 . Note that elements depicted in diagram  3600  may include structures and/or functions as similarly-named elements described in connection to one or more other drawings or as otherwise described herein, regardless of whether an implementation non-agricultural. 
     Target acquisition processor  3682  may be configured to receive data representing pixel inputs to be replicated on a surface. Object identifier  3684  may be configured to detect an image object, such as a reference with which to replicate an image. Emitter selector  3687  may be configured to select a subset of emitters responsive to inputs selecting a subset of pixels to be replicated. Trajectory processor  3683  may be configured to coordinate and manage emission of pixel projectiles, and may further be configured to generate activation signals to cause emission propulsion subsystem  3685  to propel pixel projectiles to impact a surface relative to a reference. 
     In some cases, a pixel emitter  3642   a  may include, or may be associated with, one or more pigment sources, such as pigment source  3644   a , pigment source  3644   b , and pigment source  3644   n , where pigment sources may include RED, GREEN, and BLUE pigments, or may include CYAN, MAGENTA, and YELLOW, or any other pigment combination. Trajectory  3683  may be configured to control amounts of pigments into chamber  3643  for proper color mixing. When activated, emitter propulsion subsystem  3685  may trigger chamber  3643  to propel pixel projectile  3612  from aperture  3641 . In some cases, an input  3647  is configured to push out (e.g., blow out) any remaining pigment out through output  3645  so that chamber  3643  may be used to emit other pixel projectiles of different colors. Note that the above is one example and other implements may be used to replicate an image using a pixel projectile delivery system, according to various examples. 
       FIG. 37  is a diagram depicting an example of a flow to implement a pixel projectile delivery system, according to some examples. At  3702 , data representing at least one portion of an image may be received. The portion of the image may be configured to provide a reference with which to align with a surface reference, which may be associated with a surface. Alignment of a reference of an image and a reference on a surface may facilitate synchronicity between input portions of an image to be replicated or “painted” and outputs of a pixel projectile delivery system to “paint” or emit pixel projectiles to impact a surface relative to a surface reference. 
     At  3704 , electronic communication with a computing device configured to transmit data representing simulation of an application may be established. For example, a mobile computing device (e.g., smart phone) may generate inputs describing which portions of an image are to be replicated on a surface, the communication being established between a mobile computing device and a pixel projectile delivery system. 
     At  3706 , data representing a unit of spatial translation specifying a translation relative to a reference may be received, for example, into a pixel projectile delivery system. At  3708 , one or more portions of an image associated with a unit of spatial translation relative to a reference may be detected. The unit of spatial translation may be considered an input to cause replication at a surface. At  3710 , a subset of pixels to be formed or replicated on a surface may be identified. At  3712 , emission of a subset of pixel projectiles may be caused, responsive to an input. The subset of pixel projectiles may be directed to impact a portion of a surface to form a replica of a portion of the image 
       FIG. 38  illustrates examples of various computing platforms configured to provide various functionalities to components of an autonomous agricultural treatment delivery vehicle and fleet service, according to various embodiments. In some examples, computing platform  3800  may be used to implement computer programs, applications, methods, processes, algorithms, or other software to perform the above-described techniques. 
     In some cases, computing platform  3800  can be disposed in any device, such as a computing device  3890   a , which may be disposed in an autonomous agricultural treatment delivery vehicle  3891 , and/or mobile computing device  3890   b.    
     Computing platform  3800  includes a bus  3802  or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor  3804 , system memory  3806  (e.g., RAM, etc.), storage device  3808  (e.g., ROM, etc.), an in-memory cache (which may be implemented in RAM  3806  or other portions of computing platform  3800 ), a communication interface  3813  (e.g., an Ethernet or wireless controller, a Bluetooth controller, NFC logic, etc.) to facilitate communications via a port on communication link  3821  to communicate, for example, with a computing device, including mobile computing and/or communication devices with processors. Processor  3804  can be implemented with one or more graphics processing units (“GPUs”), with one or more central processing units (“CPUs”), such as those manufactured by Intel® Corporation, or one or more virtual processors, as well as any combination of CPUs and virtual processors. Computing platform  3800  exchanges data representing inputs and outputs via input-and-output devices  3801 , including, but not limited to, keyboards, mice, audio inputs (e.g., speech-to-text devices), user interfaces, displays, monitors, cursors, touch-sensitive displays, LCD or LED displays, and other I/O-related devices. 
     According to some examples, computing platform  3800  performs specific operations by processor  3804  executing one or more sequences of one or more instructions stored in system memory  3806 , and computing platform  3800  can be implemented in a client-server arrangement, peer-to-peer arrangement, or as any mobile computing device, including smart phones and the like. Such instructions or data may be read into system memory  3806  from another computer readable medium, such as storage device  3808 . In some examples, hard-wired circuitry may be used in place of or in combination with software instructions for implementation. Instructions may be embedded in software or firmware. The term “computer readable medium” refers to any tangible medium that participates in providing instructions to processor  3804  for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks and the like. Volatile media includes dynamic memory, such as system memory  3806 . 
     Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus  3802  for transmitting a computer data signal. 
     In some examples, execution of the sequences of instructions may be performed by computing platform  3800 . According to some examples, computing platform  3800  can be coupled by communication link  3821  (e.g., a wired network, such as LAN, PSTN, or any wireless network, including WiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.) to any other processor to perform the sequence of instructions in coordination with (or asynchronous to) one another. Computing platform  3800  may transmit and receive messages, data, and instructions, including program code (e.g., application code) through communication link  3821  and communication interface  3813 . Received program code may be executed by processor  3804  as it is received, and/or stored in memory  3806  or other non-volatile storage for later execution. 
     In the example shown, system memory  3806  can include various modules that include executable instructions to implement functionalities described herein. System memory  3806  may include an operating system (“O/S”)  3832 , as well as an application  3836  and/or logic module(s)  3859 . In the example shown in  FIG. 38 , system memory  3806  includes a mobility controller module  3850  and/or its components as well as an agricultural projectile delivery controller module  3851 , any of which, or one or more portions of which, can be configured to facilitate an autonomous agricultural treatment delivery vehicle and fleet of services by implementing one or more functions described herein. 
     The structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or a combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. As hardware and/or firmware, the above-described techniques may be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), or any other type of integrated circuit. According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof. These can be varied and are not limited to the examples or descriptions provided. 
     In some embodiments, modules  3850  and  3851  of  FIG. 38 , or one or more of their components, or any process or device described herein, can be in communication (e.g., wired or wirelessly) with a mobile device, such as a mobile phone or computing device, or can be disposed therein. 
     In some cases, a mobile device, or any networked computing device (not shown) in communication with one or more modules  3850  and  3851 , or one or more of their components (or any process or device described herein), can provide at least some of the structures and/or functions of any of the features described herein. As depicted in the above-described figures, the structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or any combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated or combined with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, at least some of the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. For example, at least one of the elements depicted in any of the figures can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities. 
     As hardware and/or firmware, the above-described structures and techniques can be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), multi-chip modules, or any other type of integrated circuit. 
     According to some embodiments, the term “circuit” can refer, for example, to any system including a number of components through which current flows to perform one or more functions, the components including discrete and complex components. Examples of discrete components include transistors, resistors, capacitors, inductors, diodes, and the like, and examples of complex components include memory, processors, analog circuits, digital circuits, and the like, including field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”). Therefore, a circuit can include a system of electronic components and logic components (e.g., logic configured to execute instructions, such that a group of executable instructions of an algorithm, for example, and, thus, is a component of a circuit). According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof (i.e., a module can be implemented as a circuit). In some embodiments, algorithms and/or the memory in which the algorithms are stored are “components” of a circuit. Thus, the term “circuit” can also refer, for example, to a system of components, including algorithms. These can be varied and are not limited to the examples or descriptions provided. 
     Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described inventive techniques are not limited to the details provided. There are many alternative ways of implementing the above-described invention techniques. The disclosed examples are illustrative and not restrictive.