Patent Publication Number: US-2022211026-A1

Title: System and method for field treatment and monitoring

Description:
PRIORITY 
     The present application claims priority to International Patent Application No. PCT/CA2020/050276, filed on Feb. 28, 2020, which claims priority to Canadian Application No. 3,035,225, filed on Feb. 28, 2019. These prior applications are incorporated herein by reference in their entirety. 
    
    
     FIELD 
     This invention is in the field of drones, and more specifically to systems and methods of using an unmanned aerial or land vehicle (e.g. drone) for agricultural and/or pest control applications, such as on farms, golf courses, parks, and/or along roadways, power lines, railroads, etc. 
     BACKGROUND 
     Generally, a current farm management with a crop process  100  may be shown in  FIG. 1 . The farmer or agrologist may survey a field for a variety of weeds, fungi, or insects  102  (collectively known herein as “pests”). A pesticide, such as a herbicide, a fungicide, or an insecticide, and/or a mixture thereof, may be selected  104  and purchased from a pesticide dealer. An appropriate application time may be determined  106  and when the application time is reached, the pesticide may be broadly applied to the field. The term pesticide may include all of the following: herbicide, insecticides (which may include insect growth regulators, termiticides, etc.), nematicide, molluscicide, piscicide, avicide, rodenticide, bactericide, insect repellent, animal repellent, antimicrobial, fungicide, and any combination thereof. 
     In some instances, the appropriate application time may be a balance between a number of pests, an expense for applying the pesticide, and potential damage to the crop. If the application of the pesticide is too late, the pests may be done significant damage to the crop. If the application of the pesticide is too early, then a second application may be required later in the season resulting in additional costs. Also, broad application of pesticides may be wasteful as the application of the pesticide may be to areas of the field that do not have the pests. 
     Benefits of the aspects described herein may address disadvantages of the current farm management with the crop process. Other advantages may be apparent to a person of skill in the art upon understanding the aspects as described herein. 
     SUMMARY 
     The aspects as described herein in any and/or all combinations consistent with the understanding of one skilled in the art on review of the present application. 
     According to an aspect, there is provided a field treatment system. The field treatment system may have one or more drones receiving one or more pesticides. A base station may dispense the pesticides. One or more holding tanks may supply the base station with the pesticides. The drones may have a data collection system, a navigation system, a propulsion system, a targeting system, a treatment system, and a power source. 
     The data collection system may provide data and have at least one of: one or more positioning sensors, one or more agricultural sensors, and one or more cameras. The positioning sensors may be selected from at least one of: an altimeter, an ultrasonic sensor, a radar, a lidar, an accelerometer, a global positioning sensor, and the cameras. The one or more agricultural sensors may be configured to measure at least one of: a soil acidity, a soil moisture, a soil temperature, a conductivity, a wind direction, a wind speed, and radiation. 
     The navigation system may receive the data from the data collection system, determine a travel path of the drones, determine when an obstacle is in the travel path of the drones and adjust the travel path, and provide one or more propulsion instructions to the propulsion system in order to move the autonomous drones. The one or more drones may be selected from at least one of: an aerial drone, a rolling drone, and a combination of the aerial drone and the rolling drone. The propulsion system may comprise one or more motors turning at least one of: one or more propellers and one or more wheels. 
     The targeting system may receive the data from the data collection system, analyze the data to identifies one or more targets, provide one or more target instructions to the navigation system, determine when the drones are within a range of the treatment system and provide one or more treatment instructions to the treatment system. The treatment system may provide the one or more pesticides to the one or more targets. The treatment system may activates an eradication device directed to the one or more targets. The eradication device may be selected from at least one of: a weed trimmer, a heater, a digger, a microwave, a high energy laser, and an electric discharge. The targeting system may construct a soil profile or a plant profile. 
     At least one of: the data collection system, the navigation system, and the targeting system may be stored within a tangible computer-readable medium and may be executed by a processor within the one or more drones. The targeting system may be stored within a tangible computer-readable medium and is executed by a processor within the base station. 
     The base station comprises a refilling system for refilling one or more canisters. The canisters may be within the drones. The refilling system may have a hose under pressure and a controller to activate a valve to dispense the pesticides into the at least one canister. A pressure in the hose may be provided by a pump controlled by the controller or a gravity feed system. A weigh scale weighing the canisters may determine a full canister condition and the controller may deactivates the valve. A spill container may capture the pesticides from either a leak or the at least one canister being overfilled. A level sensor within the spill container may close the valve or deactivate the pump. A conveyor system may transport one or more empty canisters from a drone docking area to the refilling system and may transport one or more full canister from the refilling system to the drone docking area. The base station may have a battery charging system for charging the power source of the drones. 
     A communication system may enable communication between the base station and the drones, and communication between at least a pair of the drones. One or more mission rules may be transmitted between the base station and the autonomous drones. The targeting system may be configured to prioritize the at least one mission rule. 
     The field treatment system may be transportable in a self-contained trailer. 
     In another aspect, there is provided a system for monitoring a field. One or more drones may have a data collection system, a navigation system, a propulsion system, and a power source. The drones may pass over the field collecting data. The data collection system may collect the data from at least one of: at least one positioning sensor, at least one agricultural sensor, and at least one camera. The at least one positioning sensor may be selected from at least one of: an altimeter, an ultrasonic sensor, a radar, a lidar, an accelerometer, a global positioning sensor, and the at least one camera. The at least one agricultural sensor is configured to measure at least one of: a soil acidity, a soil moisture, a soil temperature, a conductivity, a wind direction, a wind speed, and radiation. 
     The navigation system may receive the data from the data collection system, determines a travel path of the drones, determines when an obstacle is in the travel path of the drones and adjusts the travel path, and provides at least one propulsion instruction to the propulsion system in order to move the drones. The propulsion system may have at least one motor turning at least one of: at least one propeller and at least one wheel. The drones may be selected from at least one of: an aerial drone, a rolling drone, and a combination of the aerial drone and the rolling drone. 
     A targeting system may receive the data from the data collection system, analyze the data to identify at least one target, and record a location of the at least one target in a target list. A communication system may enable communication of the data among the drones and a base station. The base station may transmit the at least one target to at least one rolling drone for treatment. A high clearance sprayer may receive the target list and treating the at least one target from the target list. 
     The data collection system, the navigation system, and the targeting system may be stored within a tangible computer-readable medium and is executed by a processor within the drones. The targeting system may construct a soil profile or a plant profile. 
     According to another aspect, there is provided a method for field treatment. The method may collect data with a data collection system; process the data by a navigation system to produce a travel path for at least one autonomous drone; propel the at least one autonomous drone by a propulsion system according to the travel path; and take at least one agricultural measurement from the field along the travel path. 
     The data collection system may collect the data from at least one of: at least one positioning sensor, at least one agricultural sensor, and at least one camera. The at least one positioning sensor may be selected from at least one of: an altimeter, an ultrasonic sensor, a radar, a lidar, an accelerometer, a global positioning sensor, and the at least one camera. The at least one agricultural measurement may be selected from at least one of: a soil acidity, a soil moisture, a soil temperature, a conductivity, a wind direction, a wind speed, and radiation. 
     The method may determine when an obstacle is in the travel path of the at least one autonomous drone and adjust the travel path, and providing at least one propulsion instruction to the propulsion system in order to move the at least one autonomous drone to an adjusted travel path. 
     The propulsion system may have at least one motor turning at least one of: at least one propeller and at least one wheel. The at least one autonomous drone may be selected from at least one of: an aerial drone, a rolling drone, and a combination of the aerial drone and the rolling drone. 
     The method may analyze the data by a targeting system to identify at least one target; and providing at least one target instruction to the navigation system. The targeting system may construct a soil profile or a plant profile. 
     The method may communicate among the at least one autonomous drone and a base station using a communication system. The base station may transmit the at least one target to at least one rolling drone for treatment. The method may dispense at least one pesticide from at least one holding tank into at least one canister of the at least one autonomous drone. The method may determine when the at least one autonomous drone is within a range of a spraying system and providing at least one spray instruction to the spraying system. The at least one canister may be within the at least one autonomous drone. 
     The method may treat the at least one target with at least one treatment. 
     The method may activate a valve to dispense the at least one pesticide into the at least one canister from a hose fluidly coupled to the at least one holding tank. The method may pressurize the hose with a pump or a gravity feed system. The method may weigh the at least one canister; determine a full canister condition; and deactivate the valve. The method may capture the at least one pesticide from either a leak or the at least one canister being overfilled in a spill container. The method may measure a level using a level sensor within the spill container; and cease dispensing of the at least one pesticide. The method may transport at least one empty canister from a drone docking area to a refilling system; and may transport at least one full canister from the refilling system to the drone docking area. The method may charge a power source of the at least one autonomous drone. 
     The method may transmit at least one mission rule from the base station to the at least one autonomous drone. The method may prioritize the at least one mission rule at the at least one autonomous drone. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
       While the invention is claimed in the concluding portions hereof, example embodiments are provided in the accompanying detailed description which may be best understood in conjunction with the accompanying diagrams where like parts in each of the several diagrams are labeled with like numbers, and where: 
         FIG. 1  is a block diagram of the current farm management process; 
         FIG. 2  is a side block diagram of a treatment system having a drone, a base station, and an independent pesticide holding tank; 
         FIG. 3  is a block diagram of a canister refilling system; 
         FIG. 4  is a top view block diagram of a refilling system for the base station; 
         FIG. 5  is a system diagram of a spraying system of the drone; 
         FIG. 6  is a block diagram of various electronic components of an aerial drone; 
         FIG. 7  is a block diagram of an autonomous drone farm management process having a crop phase 1 cycle advanced process; 
         FIG. 8  is a system logical architecture diagram of the treatment system; 
         FIG. 8A  is a close up view of the content on the left side of  FIG. 8 ; 
         FIG. 8B  is a close up view of the content on the right side of  FIG. 8 ; 
         FIG. 9  is a physical component architecture diagram of the treatment system; 
         FIG. 10  is a flowchart of instructions executed by the treatment system; 
         FIG. 10A  is a close up view of the content on the left side of  FIG. 10 ; 
         FIG. 10B  is a close up view of the content on the right side of  FIG. 10 ; 
         FIG. 11A  is a diagram of an onboard 12-Volt electrical power distribution system for the drone; 
         FIG. 11B  is a diagram of an onboard 48-Volt electrical power distribution system for the drone; 
         FIG. 12A  is a side view diagram of a rolling drone; 
         FIG. 12B  is a rear view diagram of the rolling drone; 
         FIG. 13  is a block diagram of an electronic system for the drone; 
         FIG. 14  is a block diagram of a pesticide system for the drone; 
         FIG. 15  is a block diagram of a light indicator system for the drone; 
         FIG. 16A  is a front view of a transportation cradle for the drone; 
         FIG. 16B  is a side view of the transportation cradle for the rolling drone; 
         FIG. 16C  is a top view of the transportation cradle for the rolling drone; 
         FIG. 17A  is a side view of a drive and suspension system for the rolling drone; 
         FIG. 17B  is a front view of the drive and suspension system for the rolling drone; 
         FIG. 18  is a schematic diagram of an onboard electrical power supply system for the rolling drone; 
         FIG. 19  is a flowchart for a sprayer image nozzle control system; 
         FIG. 19A  is a close up view of the content on the top of  FIG. 19 ; 
         FIG. 19B  is a close up view of the content on the bottom of  FIG. 19 ; 
         FIG. 20  is a process flow diagram for a sprayer system of the rolling drone; 
         FIG. 21  is a block diagram of a steering system for the rolling drone; 
         FIG. 22A  is a perspective front view photograph of an example of an aerial drone and a base station; 
         FIG. 22B  is a front view photograph of a battery refill system of the base station; 
         FIG. 22C  is a perspective side view photograph of the battery refill system of the base station; 
         FIG. 22D  is a front view photograph of an aerial drone; 
         FIG. 22E  is a perspective view of another configuration for an aerial drone; 
         FIG. 23  is a top view of an operating environment (e.g. field) for the aerial drone; 
         FIG. 24  is an example side view of the aerial drone following an altitude of the terrain; 
         FIG. 25  is a diagram of a fuse system for the rolling treatment path for the aerial drone; 
         FIG. 25A  is a close up view of the content on the left side of  FIG. 25 ; 
         FIG. 25B  is a close up view of the content on the right side of  FIG. 25 ; 
         FIG. 26A  is a perspective view of a storage housing having a plurality of compartments for aerial drones; 
         FIG. 26B  is a top view of a compartment for storing an aerial drone; 
         FIG. 26C  is a perspective front view of the compartment for storing an aerial drone; 
         FIG. 26D  is a front view of the compartment for storing an aerial drone; 
         FIG. 26E  is a side view of the compartment for storing and aerial drone; 
         FIGS. 27A and 27B  are images of the field of view of the drone demonstrating target identification; 
         FIG. 28  is a conceptual diagram of the navigation system adjusting for wind conditions; 
         FIGS. 29A and 29B  are images of the field of view of the drone demonstrating when the nozzles are to be activate; and 
         FIGS. 30A and 30B  are top down diagrams demonstrating targeting of a moving pest. 
     
    
    
     DETAILED DESCRIPTION 
     The treatment system  200  disclosed herein may comprise any number and combination of the technologies, systems, subsystems, components, processes, computations, and other items discussed or referred to herein and may also be modified or augmented with existing technologies known in the art upon review of the content herein and still be within the scope and intent of the content disclosed herein. 
     With reference to  FIG. 2 , a treatment system  200  may comprise one or more aerial drones  202 , one or more base stations  204 , one or more pesticide holding tanks  206 , and/or one or more rolling drones  1200 , as described in further detail with regard to  FIGS. 12A and 12B . In this aspect, the drone  202  may be an aerial drone  202  capable of autonomous flying over an example field  2300 . The aerial drone  202  may land on or near the base station  204  in order to receive electrical power and/or pesticide from the base station  204 . Similarly, the rolling drone  1200  may likewise be capable of autonomous movement around the field  2300  and may dock with the base station  204  in other to receive electrical power and/or pesticide from the base station  204 . In some aspects, the base station  204  may retrieve data from the aerial drone  202  and/or the rolling drone  1200 . In some aspects, the rolling drone  1200  may act as a mobile base station  204  for the one or more aerial drones  202 . The base station  204  may be supplied by a hose  208  or other type of passageway for the pesticide. The hose  208  may be connected to the tank  206  using a shutoff valve  210  that enables or disables fluid discharge from the tank  206 . The shutoff valve  210  may be a manual valve and/or an automatic valve controlled by the base station  204 . The tank  206  may be an independent pesticide holding tank. The tank  206  may be supported above the ground using a support structure  212  having a number of support legs  214 . 
     In some aspects, the treatment system  200  may be fully transportable, such as within a self-contained trailer (not shown) that may be pulled behind a truck or tractor (not shown). In other aspects, the treatment system  200  may be mobile having one or more wheels, steering, and drive motor. In yet another aspect, the base station  204  and holding tank  206  may comprise one or more propulsion devices capable of transporting the base station  204  and holding tank  206  through the air. Although a single holding tank  206  is demonstrated herein, other aspects may have more than one holding tank  206  comprising different types of pesticides. Further details of these particular components of the treatment system  200  may be described in further detail herein. 
     The treatment system  200  may comprise one or more mission rules, which may be stored in whole or in part in a mission rules database  824 , a communication system  614 ,  1324 ,  2002  allowing communication by and between the systems and subsystems, such as, the drones  202 ,  1200 , a data collection system  848 , a navigation system  608 , a targeting system  842 , a spraying system  500 , a mechanical control system  650 , and/or the mission rules which may be the same, in part, across multiple missions and specific, in part, to a particular mission or portion of a mission to be executed by the treatment system  200 . The mission rules specific to a mission may govern the mission execution in whole or in part and communicate all or part of the information, data and parameters that the drones may use to execute the mission. One or more of the drones  202 ,  1200  may be deployed after receiving all or part of the information, data, and/or parameters the drones  202 ,  1200  required to execute a mission. The drone  202 ,  1200  may receive portions of the mission rules after the drone  200 ,  1200  has been deployed and during execution of the mission and after the mission is executed, for example, directing the drone  202 ,  1200  to execute another mission. The treatment system  200  may use a single drone  202 ,  1200  or multiple drones  202 ,  1200  working together for the mission. If using multiple drones  202 ,  1200 , each of the multiple drones  202 ,  1200  may have the same, similar, and/or different mission tasks and/or mission rules. 
     The mission rules may comprise one or more optimization rules to be applied and may modify the optimization rules during the mission and/or allow the targeting system  842  to modify the optimization rules during the mission. If the predictive analysis subsystem  822  cannot make the determination with the current mission rules database  824 , the targeting system  842  may flag the object to the data collection system  848  so that the data collection system  848  may gather additional data regarding the object. The data collection system  848  and the predictive analysis subsystem  822  may both be capable of processing multiple objects and/or groups of objects simultaneously as described herein. The data collection system  848  and the predictive analysis subsystem  822  may be stored and/or executed in the drones  202 ,  1200  and/or the base station  204 . The targeting system  842  may be a distributed system contained in one or more drones  202 ,  1200  and/or the base station  204  or other mission command center  932 . 
     The treatment system  200  may be used to accomplish mission objectives within a specific geographic treatment area according to the mission rules. The mission rules may define one or more boundaries and some of the content of the treatment area. The mission rules may provide instructions, data, computational parameters, constants, and/or variables to the treatment system  200  to allow the treatment system  200  to identify objects within the treatment area to be treated and to distinguish objects to be treated from objects that are not to be treated and from objects that are to be ignored or otherwise dispositioned. The treatment system  200  may execute the treatment mission whereby one or more drone systems  202 ,  1200  may be used to gather information. 
     The treatment system  200  may execute a treatment mission whereby one or more drones  202 ,  1200  may be used to identify objects within the treatment area to be treated to allow the objects so identified to be distinguished one from another. The drone  202 ,  1200  may be used to identify objects to be treated from objects that are not to be treated and from objects that are to be ignored or otherwise dispositioned. The treatment system  200  may execute a treatment mission whereby one or more drones  202 ,  1200  may be used to treat the objects identified for treatment. 
     Turning to  FIG. 3 , a block diagram of a refilling system  300  for refilling one or more canisters  302 . A pump  304  may provide pressure for the pesticide within the hose  208  enabling the hose  208  to dispense the pesticide into the canister  302 . When the pressure within the hose  208  reduces below a threshold pressure as measured by a pressure sensor  1404 , a controller  308  may activate the pump  304  in order to increase the pressure within the hose  208 . A valve  306  may be selectively turned on and off using the controller  308 . The valve  306  may output the pesticide into a funnel  310  above the canister  302  in order to reduce or prevent spillage. In some aspects, the funnel  310  may be attached to the canister  302  while in other aspects, the funnel  310  may be suspended above the canister  302 . In another aspect, the pressure may be maintained within the hose  208  using a gravity feed system rather than the pump  304 . In the gravity feed system, when the pressure sensor  1404  falls below the threshold pressure, an alert message may be transmitted by the controller  308  to the mission command center  932  indicating that the holding tank  206  is empty. 
     A weigh scale  312  may weigh the canister  302  and an amount of pesticide being dispensed into the canister  302 . The controller  308  may periodically initiate a reading from the weigh scale  312 . If the controller  308  does not detect the weight of the canister  302  on the weigh scale  312 , then the controller  308  maintains the valve  306  in a closed position. When a canister  302  is in position on the weigh scale  312 , the controller  308  detects the canister  302  as the reading exceeds a canister weight threshold. The controller  308  may then initiate a refilling process as described below. 
     When the canister  302  has been detected by the controller  308 , the controller  308  may periodically initiate the reading from the weigh scale  312  and may compare this reading with a weight corresponding to a full canister  406 . If the reading from the weigh scale  312  is below the weight corresponding to a full canister  406  (e.g. filled with pesticide), then the controller  308  may activate the pump  304  and/or initiate an opening of the valve  306 . When the reading is equal to or exceeds the weight corresponding to the full canister  406 , the controller  308  initiates a closure of the valve  306  and/or deactivates the pump  304 . A sampling rate of the weigh scale  312  may be sufficiently fast in order to determine when the canister  302  is full in order to prevent spillage. A size of the canister  302  may be dependent on the size of the field, the type of pesticide, and/or the size of the drone  202 ,  1200 . In some aspects the canister  302  may be pressurized and a pressurized refilling system may be used to fill the canister  302 . In other aspects the canister  302  may be unpressurized and use a pumping mechanism or gravity feed. 
     In another aspect, the refilling system  300  for refilling the one or more canisters  302  may be surrounded by a spill container  314 . If the container  302 , the hose  208 , the valve  306 , and/or the pump  304  happens to leak or the container  302  is overfilled, the spill container  314  may collect a spilled pesticide therein. A level sensor  316  may trigger a signal sent to the controller  308  and in response, the controller  308  may deactivate the pump  304  and/or close the valve  306 . In some aspects, the controller  308  may close the shutoff valve  210  at the tank  206 . The controller  308  may initiate a message send to the command center  932  prompting a maintenance person. When the maintenance person has corrected a cause of the spill or leak, the maintenance person may drain the spill container  314  using a drain valve  318 , which may be a manual valve or an automatic valve initiated by a button sending a signal to the controller  308 . 
     Turning to  FIG. 4 , the refilling system  300  may work in conjunction with a conveyor system  400 . The conveyor system  400  may comprise a conveyor  402  that may transport empty canisters  302  from a drone landing or docking area  404  to the refilling system  300  in order to be refilled. The docking area  404  may be part of the base station  204 . Once filled, the conveyor  402  may transport the full canisters  406  from the refilling system  300  to the drone landing or docking area  404 . The controller  308  may control the conveyor  402  so that the conveyor  402  only operates when the drone  202 ,  1200  has docked with the docking area  404 . In this aspect, the entire conveyor  402  and docking area  404  may be surrounded by the spill container  314 . In this aspect, the conveyor  402  may be a continuous conveyor. 
     In another aspect, the refilling system  300  may fill the drone  202 ,  1200  without transporting empty canisters  302 . For example, the drone  202 ,  1200  may comprise a refill port (not shown) fluidly coupled to the onboard canister  302 . The refill port may be connected to the hose (e.g. umbilical) of the refilling system  300  when the drone has landed or docked with the base station  204 . The refilling system  300  may dispense the pesticide into the refill port in order to fill the onboard canister  302 . 
     The drone  202 ,  1200  may have a housing  1208  coupled to one or more motors  610  with a frame  1604 . In this aspect, the housing  1208  may be a generally square or a rectangular box with a generally hollow interior for holding one or more components  600  as described in further detail below. For the aerial drone  202 , the one or more motors  610  may spin one or more propellers  620  using one or more gears  622 . The propellers  620  may be protected using one or more guards (not shown) that may be coupled to the motors  610  or the frame  1604 . A agricultural sensor probe (not shown) having one or more agricultural sensors  612  may be present on proximate to a bottom of the housing  1208  and configured to contact the ground when the aerial drone  202  has landed and/or be extended to contact the ground for the rolling drone  1200 . The one or more components  600  within or mounted to the housing  1208  may comprise one or more printed circuit boards (PCBs) (not shown) having a number of electronic components and/or electromechanical components. 
       FIG. 5  demonstrates a spraying system  500  of the drone  202 ,  1200 . The spraying system  500  may comprise a quick connect  502  for receiving the full canister  406  or discharging the empty canister  302 . In some aspects as previously described, the drone  202 ,  1200  may comprise the refill port and therefore would not require the quick connect  502  in order to fill the canister  302 . The quick connect  502  may be activated using an air pump  504  such that when the air pump  504  is activated, the quick connect  502  releases the empty canister  302 . The air pump  504  may receive air from an air intake  506  and be supplied with electrical power from either a battery  508  and/or a power supply  510  of the base station  204 . In this aspect, the battery  508  may be connected using a battery quick connect  512  and/or the power supply  510  may supply power using a power umbilical  514 . The pesticide from the full canister  406  may be provided to a solenoid valve  516  via one or more internal hoses  518 . The solenoid valve  516  and/or the air pump  504  may be controlled by a drone controller  600  within the drone  202 ,  1200 . When pesticide is to be expelled from the drone  202 ,  1200 , the drone controller  600  opens the solenoid valve  516  permitting the pressurized pesticide from the full canister  406  to be expelled out of one or more spray nozzles  520 . 
     Although the term spraying system  500  is used herein, this term is not intended to be limiting. The spraying system  500  may comprise a chemical spray and/or a chemical applicator such as a wick or swab applicator. The spraying system  500  as used herein may also comprise any other system of treatment that deploys one or more chemical and/or organic substance in response to a command. The drone  202 ,  1200  may comprise a non-chemical treatment system that may deploys a non-chemical treatment. The non-chemical treatment may comprise an energy treatment such as heat (or fire), sound, radiation, electricity, mechanical removal, etc. The spraying system  500  may comprise both chemical and non-chemical treatment applications. 
     The spraying system  500  may include one or more spray nozzles  520  mounted on the drone  202 ,  1200 , on one or more spray arms or booms  1222  that may be mounted upon the drone  202 ,  1200 , or a combination thereof. The spraying system  500  may be attached to the drone  202 ,  1200  using a 2-axis or 3-axis mechanical gimbal mount (not shown), servomotors  610 , and/or other electro-mechanical components capable of controlling a spray nozzle vector including pitch, elevation, yaw, azimuth, etc. through the spray arms  1222  and/or the spray nozzles  520 . The spraying system  500  may include the servomotor(s)  610  within the drone  202 ,  1200  configured to drive the gimbal to the identified target. The spraying mechanism may comprise one or more valves  1224  within the spraying system  500  may be configured to activate the spray, in response to a spraying command from a processor  602 . The spraying mechanism within the spraying system  500  may be configured to adjust the spray nozzle  520  to change a spray geometry from a fine stream to a broader stream, to a fine mist, and/or to any combination therebetween. In some aspects, the treatment may be applied automatically when the drone  202 ,  1200  lands on (or arrives at) a treatment area described in further detail herein. 
     In some aspect, the spraying system  500  may be a part of or be coupled to the mechanical system  650  in order to move one or more spray arms or booms  1222  and/or one or more spray nozzles  520 . A spray application, also referred to as a spray event, may be accomplished by aiming the spray nozzle  520  at the target and opening a spray valve  1224  to release the spray chemical and closing the spray valve  1224  to stop the release of the spray chemical. In some aspects, the treatment may be applied automatically when the drone  202  lands on, hovers over, and/or is generally in proximity of (or for rolling drones  1200  arrives at) the treatment area. 
     As shown in  FIG. 6 , a computing system  600  for the drone  202 ,  1200  is demonstrated. The computing system may comprise a processor  602  may execute computer-readable instructions from a tangible computer-readable medium  604  (e.g. memory) and the processor  602  may store data to the memory  604 . The processor  602  may execute instructions in order to capture image data from one or more camera(s)  630 . The camera(s)  630  may have a field of view generally below and/or in front of the drone  202 . At least one of the camera(s)  630  may have a field of view generally in a direction of motion of the drone  202 ,  1200 . In some aspects, at least one of the camera(s)  630  may automatically change direction to the direction of motion of the drone  202 ,  1200 . In other aspects, the drone  202 ,  1200  may rotate in order to align the field of view along the direction of motion of the drone  202 ,  1200 . 
     The drone  202 ,  1200  may comprise a data collection system  848 . The data collection system  848  may comprise any one of or any combination of one or more cameras  630 , one or more sensors  606 ,  612 , and/or other data gathering devices. It is to be understood that the data collection system  848  may include an array of various different sensors  606 ,  612  configured to collect data within a predefined proximal distance from the drone  202 ,  1200 , and transmit the sensor/image data back to the internal software systems of the drone  202 ,  1200  (e.g., the targeting system  842 , the spraying control, the spray vectors engine) and/or to the base station  204  and/or a display device of mission command center  932  for outputting to an operator. The data collection system  848  may provide data to identify objects. This object data may be provided to the targeting system  842  which uses the mission rules and object comparison data to determine if the object is the target for this mission, the target for another mission, the non-target to be protected from spraying, an obstacle to be avoided and/or an object to be ignored. 
     Various aspects of the drone  202 ,  1200  may comprise systems or subsystems for automatic detection of objects and for determining if an object is to be treated according to the mission rules. An object to be treated according to the mission rules may be sometimes referred to as a target. An object that is identified whereby treatment of said object is to be expressly avoided according to the mission rules may be sometimes referred to as a non-target. Various aspects of the drone  202 ,  1200  may include capabilities for automatic detection of objects that the drone  202 ,  1200  may physically avoid according to the mission rules or objects to be ignored altogether in this mission or future missions. The mission rules may be used to differentiate between objects, determine if an object is to be avoided with treatment, to select targets to be sprayed, to prioritize targets, to deselect targets, to re-select targets. The mission rules may be used to determine when, where and how the navigation system  608  may use active stabilization. The mission rules may be used to may include spray vector solutions or be used by the drone  202 ,  1200  to determine spray vector solutions automatically. The mission rules may be used to achieve target identification, single instance target tagging, continuous and intermittent target tracking, etc. 
     Various aspects of the drone  202 ,  1200  may comprise a communication system  614 , a data collection system  848 , the navigation system  608 , the targeting system  842 , the spraying system  500 , and/or a treatment success verification system. The drone communications system  614  may comprise one or more wireless communication devices and/or a chipset such as a Bluetooth™ device, an 802.11 or similar device, a satellite communication device, a wireless network card, an infrared communication device, a Wi-Fi device, a long range antenna (LoRa), a Real-Time Kinematic (RTK) antenna, a WiMAX device, a cellular communication device, etc. and other communication methods, devices and systems available or available in the future. The drone  202 ,  1200  may also comprise a data bus between onboard systems and subsystems. 
     In some aspects, the camera(s)  630  may be affixed or integrally formed with a body of the drone  202 ,  1200 . In other aspects, the camera(s)  630  may be extended on an arm  1203  that may rotate 360-planar degrees and/or extend up to 2 meters outside of perimeter of the drone  202  (e.g. a circumference of the drone  202 ,  1200 ). By placing the camera(s)  630  on the arm  1203 , the camera(s)  630  may be positioned in a way such that the image may be taken before a propeller wash for aerial drones  202 . This configuration may permit more clear images to be captured before the propeller wash, which causes the plants to be buffeted around and/or sideways. In another aspect, the camera(s)  630  may be located on a gyroscope or other stabilizing apparatus to minimize jitter and/or shaking of the camera(s)  630 . The arm  1203  may also have some mechanical components (not shown) to adjust a camera angle slightly to follow an incline of a terrain of the field. For example, when the drone  202  travels down a steep incline, the camera(s)  630  may image the field at a slightly inclined angle such as to make the images appear “flat” or consistent to an artificial intelligence (AI) framework  1920 . In other aspects, digital post processing may correct for any distortion and/or blurriness of the camera(s)  630 . 
     The camera(s)  630  may comprise a lens, a filter, and an imaging device, such as a CCD or CMOS imager. In some aspects, the filter may only permit certain wavelengths of light to pass through and be captured by the imaging device. For example, the filter may only permit infrared light to pass through. In another example, the filter may only permit ultraviolet light to pass through. In yet another example, the filter may only permit visible light to pass through. The visible light filter may be a filter mosaic in order to permit the image sensor to capture red-green-blue (RGB) colored light. In another aspect, the filter mosaic may also include infrared, ultraviolet light filters, and/or any number of filters, such as 10 bands) that divide light into specific frequency bands. The frame rate of the imaging device may be selected based on the number of filters, such as 30 frames-per-second (fps) per filter. In this aspect, the imaging device may have five filters and therefore the imaging device may have a frame rate of at least 150-fps. In other aspects, the frame rate may be higher or lower for a particular filter. According to some aspects, the camera(s)  630  may capture image data at 30 frames-per-second at a 4k resolution or greater. The processor  602  may be configured to perform image processing on the captured image data as described in further detail below. 
     In some aspects, the drone  202 ,  1200  may comprise one or more light emitting diodes (LEDs) for projecting light from the drone  202 ,  1200  into the field of view of at least one of the cameras  630 . The LEDs may project infrared light, ultraviolet light, red light, blue light, green light, white light, and/or any combination thereof. In some aspects, the processor  602  may modulate the LEDs and/or control an on/off state. In some aspects, the LEDs may start with wavelengths not visible to most pests in order to more accurately determine their position without disturbing the pests. 
     The processor  602  may read position data from one or more positioning sensor(s)  606 , such as an altimeter, ultrasonic sensors, radar, lidar, accelerometers, etc. In some aspects, the positioning sensor(s)  606  may be a pair of cameras  630  capturing binocular vision from the drone  202 ,  1200 . In some aspects, the processor  602  may triangulate a position of one or more features external to the aerial drone  202  in order to assist with navigation by a navigation system  608 . The navigation system  608  may provide instructions to the one or more motors  610 . In this aspect, the navigation system  608  may be performed using the processor  602 . In another aspects, the navigation system  608  may be independent of the processor  602 . 
     In another aspect, the navigation system  608  may comprise one or more navigation and/or positioning sensors  606 , such as a GPS system, an altimeter, ultrasonic sensors, radar, lidar, etc. In some aspects, the positioning sensor  606  may be a pair of cameras  630  capturing binocular vision from a separate drone  202 ,  1200  or a remotely located and fixed-position binocular camera system  630 , such as a pole-mounted camera system. In some aspects, the processor  602  may triangulate one or more locations of one more feature external to the drone  202 ,  1200  and triangulate a drone position using the one or more features external to the drone  202 ,  1200  in order to assist with navigation by the navigation system  608 . The navigation system  608  may receive input from the data collection system  848  to assist with navigation. The navigation system  608  may track a specific location of the drone  202 ,  1200  relative to a previous location and may do so continuously in order to command the drone motors  610  to propel the drone  202 ,  1200  to follow a desired path from the base station  204  to a treatment area and then within the treatment area. 
     The navigation system  608  may provide instructions to control the movement of the drone  202 ,  1200 . The navigation system  608  may determine a first drone location and/or orientation, then be provided a desired second drone location and/or orientation, calculate a propulsion to move the drone from the first location to the second location and issue commands to move the drone  202 ,  1200  in any number of desired directions, orientations, velocities and/or accelerations. The navigation system  608  may comprise internal processors (not shown) to calculate the propulsion and/or may rely on processing resources  602  external to the navigation system  608  to calculate the propulsion with the navigation system  608 . The navigation system  608  may issue commands to the drone mechanical system  650 , such as motors  610  and gears  622 , to control the propulsion system  620 , such as wheels  1206  and/or propellers  620 , to control the movement of the drone  202 ,  1200 . The control and movement may include commands directed to pitch, elevation, yaw, azimuth, forward, backward, left, right, etc. 
     The accelerometers may be used to detect and respond to drone  202 ,  1200  accelerations and vibrations. Such accelerations and vibrations may be caused by weather, terrain, other external influences, and/or mechanical vibration and movement of the drone  202 . The drone  202 ,  1200  may include rate gyros to stabilize the drone  202  and magnetometers and accelerometers used for canceling gyro drift. The global positioning system components or other positioning devices  606  may be included to determine the drone location, heading, and velocity to compute spraying solutions, and to target known treatment target coordinates such as a tree stump or other woody plants that are designated for repeated spraying across multiple missions. 
     The drone  202 ,  1200  may comprise the drone mechanical system  650  and the drone mechanical system  650  may comprise a propulsion system  620 . The mechanical system  650  may comprise motors  610  driving a transmission system  622 , including gears  622 , that may in turn drive shafts (not shown) that may drive wheels  1206 , rotors or similar components or any combination thereof to create propulsion. The mechanical system  650  may comprise direct drive motors  610  not requiring gears  622  or a combination of direct drive and/or gear drive components. The mechanical system  650  may be commanded by a mechanical control system  650  and/or receive commands directly from the navigation system  608 . The mechanical system  650  of the drone may comprise one or more motors  610  to move the drone  202 ,  1200  to the second location. 
     The drone  202 ,  1200  may have one or more agricultural sensors  612  located on a sensor probe (not shown) or alternatively on the wheel  1206 . The processor  602  may periodically instruct the navigation system  608  to land the drone  202  or instruct the probe to move into the soil for the rolling drone  1200  at positions in a field. When the drone  202 ,  1200  has landed or reached a sufficient distance depending on whether or not the sensor  612  requires contact with the field, the processor  602  may read agricultural data from one or more agricultural sensors  612 , such as soil acidity, soil moisture, temperature, conductivity, wind, gamma radiation sensor, and/or other radiation sensors, etc. used to construct a soil profile and/or a plant profile. 
     In some aspects, the sensors  612  may be inserted into the soil via a hydraulic press, auger system, located on the wheel  1206 , and/or combination thereof and the sensor  612  may record measurements within the soil and thereby reducing or eliminating the need to collect soil. In another aspect, the sensor  612  may not be inserted into the soil but rather the soil may be collected via an auger system (not shown) or a grapple (not shown) and analyzed by one or more sensors  612  within the drone  202 ,  1200 . In yet other aspects, the sensor  612  may not be located on or within the drone  202 ,  1200  and the drone  202 ,  1200  may collect the soil via the auger system or the grapple and may store the soil in a soil canister (not shown) for analysis by the base station  204  and/or delivered to a laboratory. In other aspects, the sensors  612  may be able to remotely sense without requiring physical contact with the soil. For example, one or more sensor readings may be performed by measuring radiation, magnetic fields, and/or spectral analysis. In some aspects, a liquid application system (not shown) may apply a liquid, such as water, to the soil to facilitate softening the soil for collection. 
     According to some aspects, the processor  602  may perform image processing on the captured image data at a location in order to determine one or more of these characteristics as described in further detail herein. 
     The processor  602  may communicate via a wireless transceiver  614 . The wireless transceiver  614  may communicate using Wi-Fi, Bluetooth, 3G, LTE, 5G and/or a proprietary radio protocol and system, etc. The processor  602  may communicate with the base station  204  in order to relay status data, such as fuel, battery life, pesticide amount, position, etc. and/or agricultural data. In another aspect, the status data and/or agricultural data may be stored in internal memory, such as an SD card and/or a hard drive) until the processor  602  is within communication range (e.g. the wireless transceiver  614  has a stable connection with the base station  204  or when the drone  202 ,  1200  docks with the base station  204 ). 
     The drone  202 ,  1200  may have one or more sprayers  500  for spraying or depositing a herbicide, pesticide, and/or fungicide. The sprayer  500  may have a spraying distance of between 0 to 3-ft with a targeting area of 4-inches by 4-inches or some other quanta on the ground and a spraying orientation. Some aspects may have the sprayer  500  capable of the spraying distance of 6-inches to 20-inches. In some aspects, multiple sprayers  500  and/or adjustable sprayers  500  may be used depending on a mode that corresponds to a higher concentration of pests in one area (e.g. may spray higher and/or wider). 
     In some aspects, the spraying orientation and distance may be adjustable. For example, the sprayer  500  may be located on a boom arm  1222  which may be retracted and/or repositioned in a 360-degree pattern. A vertical boom  1222  may adjust a height of the sprayer  500 . In another aspect, one or more booms  1222  with one or more sprayers  500  may be present. In yet another aspect, a bar having one or more vertical sprayers may be positioned approximately 6-inches apart. The vertical sprayers may move 2 or more-inches in each direction along the bar creating a “dot-matrix printer”-like effect where the nozzles  520  may be repositioned. In another aspect, the pesticide may be applied using physical contact, such as wicking, to paint on the pesticide contained in a sponge-like material. 
     In an aspect, the sprayer  500  may have a number of reservoirs (e.g. canisters  302 ) for holding one or more herbicides, pesticides, and/or fungicides. On detection of a weed  2320 ,  2322  by the processor  602  as described in further detail below with reference to  FIG. 23 , the drone  202 ,  1200  may land or approach within the spraying distance and/or may approach the spray area within the spraying distance. The processor  602  may then select the appropriate reservoir based on a type of weed  2320 ,  2322  and initiate an actuator in order to spray the chemical and/or powder onto the weed. In another aspect, instead or in addition to the sprayer  500 , the processor  602  may instruct a microwave or high energy laser beam directed at the weed  2320 ,  2322 . In another aspect, the processor  602  may instruct the drone  202 ,  1200  to land on or approach the weed  2320 ,  2322  and activate a weed eradication device (not shown), such as a weed trimmer, heater, sprayer, digger, microwave, high energy laser, electric discharge, etc. 
     In another aspect, on detection of the weed  2320 ,  2322  by the processor  602 , the processor  602  may record the GPS/RTK coordinate data and/or other spatial sensing data (e.g. accelerometers, etc.) to determine the spray location without the use of cameras. The GPS/RTK coordinate data may then subsequently be used by a spray drone  202 ,  1200  that performs treatment of the one or more identified weeds. 
     In another aspect, the sprayer  500  may have a single reservoir (e.g. canister  302 ) for holding only one selected pesticide. The processor  602  may be configured to identify only the type of weed  2320 ,  2322  capable of being treated with the selected pesticide. In such a treatment system, a plurality of drones  202 ,  1200  may each contain different selected pesticides and each processor  602  within each drone  202 ,  1200  may identify their respective type of weed  2320 ,  2322 . 
     A battery  618  may be used to power the motors  610  and the other electronic components  600 . In some aspects, the battery  618  may only be used to power the other components  600  and a gasoline, hydrogen, or other combustible fuel engine may be used to power the motors  610 . The motors  610  may be coupled to one or more propellers  620  via one or more gears  622 . One or more chargers  624  may be used to recharge the battery  618 . 
     Turning to  FIG. 7 , an autonomous drone farm management process  700  having a crop phase 1 cycle advanced process is shown. Steps  102  and  104  may be the same as previously described with reference to  FIG. 1 . In this aspect, the drone  202 ,  1200 , in particular to aerial drone  202 , may perform a scanning process  702  as described in further detail below with reference to  FIG. 23  in order to locate any pests in the field. During the scanning process  702 , the base station  204  and/or the mission command center  932  may include a rules data store  810  which may include identification rules for plants, pests or other target types. The rules data store  810  may include one or more target selection and target priority rules. The rules data store  810  may include one or more spraying rules, and other chemical application rules specific to the mission, the chemical(s) being applied, the target, and any other data input. The rules data store  810  may include treatment rules, spraying rules, and/or other chemical application rules specific to the mission, including the chemical(s) being applied, the target, and any other data that may be useful in processing and understanding camera/sensor data input. The drone  202 ,  1200  may include an local onsite data store  824  within the memory  604  which may include identification rules for specific targets, non-targets, plants, pests or other types of objects. The rules data store  810  may include object or target identification, selection and prioritization rules. 
     Periodically, a treatment action  704  may be adjusted. In general, a broad-scope aerial survey may be performed at high altitude in order to identify key areas requiring treatment within a  1 - m  by  1 - m  space, or other size depending on a resolution of the system. For each of these treatment areas, a low-altitude drone  202 ,  1200  may survey at a lower altitude (e.g. high resolution) and may determine one or more precise coordinates of pests to spray. A pesticide application process  706  may then instruct one or more of the drones  202  to apply the pesticide directly to each area of the field impacted by the pest and/or directly to the pest itself. In alternative aspect, the pesticide application process  706  may provide the location and/or coordinates of the identified pests to a manually controlled system, such as a high clearance sprayer or may provide a map to a farmer with a manual sprayer. 
     As presented in  FIGS. 8, 8A and 8B , a system logical architecture  800  for the treatment system  200  may comprise a number of user interfaces, application program interfaces (APIs), databases, artificial intelligence modules, and/or control modules. The system logical architecture  800  may have one or more field condition user interfaces  802 ,  804  on the mission command center  932  systems for entering and/or importing field condition data into a pest treatment central database  810 . A field treatment planning user interface  806 , such as on a computer of the farmer, may permit a user to determine a field treatment plan for a particular field from the pest treatment central database  810 . Once a field plan has been devised, a job scheduler  850  may be executed that assigns one or more jobs through a job assignment API  812 . 
     A job assignment user interface  814  may access the job assignment API  812  in order to assign jobs to one or more missions and a mission assignment user interface  818  providing input to a mission layout per job module  816 . The mission layout per job module  816  may receive field data from a field data API  820 . A mission planning artificial intelligence module  822  may generate the one or more missions per job based on the data provided by the mission layout per job module  816 . The mission data may be stored in the on-site mission rules database  824 , which may be accessed by a mission status user interface  826  in order to display mission status data. The mission data may also be transferred to the pest treatment database  810  using a job results API  832 . 
     The drone  202 ,  1200  may comprise the targeting system  842  which may receive input data from various data sources, and analyze the data to identify, select, and prioritize targets, track real-time or near-real-time relative target location, calculate and converge on spraying solutions, and activate and control drone spraying. The targeting system  842  be an integrated system with common control modules or may be implemented as a separate targeting system  842  with onboard target control module to be used in conjunction with a spraying system  500  having an onboard spray control module. The targeting system  842  may receive data from one or more cameras  630  and/or one or more sensors  606 ,  616  controlled by the data collection system  848 . The data may include drone location data, drone movement vectors, drone vibration data, weather data, target images, distance/range data, infrared data, GPS/RTK data, and any other sensor data described herein. 
     In one aspect, the drone  202 ,  1200  may include a process by which a drone  202 ,  1200  may identify target characteristics, calculate a spray vector, position a spray nozzle in accordance with the spray vector, position the drone  202 ,  1200  in accordance with the spray vector and spray the target. The steps in this process may be performed by one or more components in the drone  202 ,  1200  such as the targeting system  842  and the subsystems thereof, in coordination with the drone motor components, the camera/sensor, and/or various remote and external systems that may include an operator interface as described. 
     In another aspect, the base station  204  may identify target characteristics, calculate a spray vector, position a spray nozzle in accordance with the spray vector, position the drone in accordance with the spray vector and spray the target. The steps in this process may be performed by one or more components in the base station  204  and relayed to the drones  202  such as the targeting system  842  and the subsystems thereof, in coordination with the drone motor components, the camera/sensor, and/or various remote and external systems that may include an operator interface. In another aspect, the base station  204  may relay an array of target coordinates to a high clearance sprayer that may be used to instruct the nozzles to turn on and off when the nozzle coordinates correspond to the target coordinates. 
     Once the layout missions per job module has planned the missions, the layout missions per job module may initiate a deployment module  828  to deploy one or more drones  202  according to their respective mission plan. Each of the drones  202  may execute a target boundary navigation module  830  that ensures the drone  202  remains within the mission plan parameters. 
     The mission rules may result in the drone  202 ,  1200  prioritizing targets. For example, the system logical architecture  800  may identify multiple targets in close proximity to one another such that the probability of effectively spraying all proximal targets with a single spray is within acceptable limits. Another example is the case where the mission rules result in target sequencing whereby the system logical architecture  800  optimizes the number of targets the drone  202 ,  1200  can spray for a given rate of drone travel. Further, the mission rules may dictate a target sequence based on a desired lag time between sprays. The mission rules can be modified once or multiple times during the mission to optimize the target treatment. 
     In addition to modifying the mission rules, an operator or the system logical architecture  800  may modify the priorities within the mission rules. The operator may be an artificial intelligence engine. For example, targets may have different characteristics such as type or size or proximity to the drone or proximity to a non-targeted plant or object. Any one or all of these may generate different spraying priorities. Thus, the system logical architecture  800  may be required to prioritize the targets as the targets are identified. The prioritization process may be included in the identification or verification steps or may be a separate step. The prioritization may result in targets being tagged for later treatment or ignored. The prioritization may affect the order in which the targets are sprayed, or which spray nozzle  520  is used. The prioritization may result in multiple spray nozzles  520  being used to treat the same target. The prioritization may affect calculation of spray vectors. In some aspects, the prioritization may determine a type of treatment, such as, for example, larger targets may receive chemical treatment whereas small targets may receive an energy treatment. 
     For example, a first prioritized target may be sprayed in a first manner with the same spray vector calculation. A second prioritized target may be sprayed in the same manner, but the set of initial conditions for target may be a set of spray conditions for target. Similarly, a third prioritized target may be sprayed in the same manner, but a set of initial conditions for target may be the set of spray conditions for target. The targets may be grouped by the targeting system  842  as targets to be sprayed with a single spray and as such have been given by the targeting system  842  a single collective target contact area with a single center point for all three targets. The targeting system  842  calculates and executes a single spray vector for the collective target contact area with a spray geometry that the targeting system  842  selects for the single collective target contact area. 
     The navigation module  830  may receive location and/or orientation data via a location/orientation collection module  834 . Obstacles may be avoided using an obstacle avoidance module  836  that may receive one or more images from an image collection module  838 . The obstacle avoidance module  836  may perform computer vision in order to determine if the obstacle is likely to interfere with the mission plan. The navigation module  830  may provide data to an actionable data identification module  840 , which may also receive images from the image collection module  838 . 
     The targeting system  842  determines if the object is an obstacle by referencing obstacle data or using an obstacle algorithm or both. If the object is determined to be an obstacle, the targeting system  842  flags the obstacle to the navigation module  830  to become a navigation factor. If the targeting system  842  determines the object is to be ignored, then the targeting system  842  flags the object to be ignored and takes no further action regarding the object so flagged. If the targeting system  842  determines the object is a non-target, the targeting system  842  flags the object as a non-target to be avoided by the spray and the non-target so flagged becomes a factor used in calculating one or more spray vectors. 
     The actionable data identification module  840  may determine when a treatment action is required and initiate instructions to a treatment action API  842 . The actionable identification module  840  may perform a crop/non-crop detection AI module  844  and/or a plant species detection AI module  846 . In some aspects, the crop/non-crop AI module  844  and/or the plant species detection AI module  846  may be configured to detect pests, disease, such as weeds, fungus, insects, etc. In other aspects, the pests and/or disease detection may be performed in dedicated modules or systems. These two modules  844 ,  846  assist the actionable data identification module in determining where the treatment action is required. All of the data provided by the navigation module  830 , the actionable data identification module  840 , and the treatment action API  842  may be stored in the on-site mission rules database  824  using a mission data collection module  848 . 
     In one aspect, the drone  202 ,  1200  may detect objects and identify and verify one or more targets, using the camera  630  and/or the sensors  606 ,  612  and may use additional data sources. For example, the image data from cameras  630  and the sensor data from the sensors  606 ,  612  may be used to detect one or more objects. The same data or additional data may be used to identify the object as a target or potential target. The object may be tagged for further analysis prior to being added to the target list, being tagged or being ignored. The further analysis may be performed using the same or additional data such that the drone is made to collect additional data for analysis. In this way a predictive first analysis may be performed that requires fewer analysis resources and reduced analysis time. The predictive first analysis can be used to optimize the drone resources  600  and only commit drone system resources  600  to objects that are predicted to be targets. The predictive first analysis may be followed by a second analysis or a series of analysis prior to being added, or not, to the target list. An object may be added to the target list based on one, two, or any number of analysis cycles consistent with mission rules. The target list may be verified prior to or after a spray vector has been calculated. 
     In another aspect, the base station  204  may detect objects and identify and verify one or more targets, receiving data from the cameras  630  and/or the sensor units  606  of the drones  202  and may use additional data sources. For example, the image data and the sensor data may be used to detect one or more objects. The same data or additional data may be used to identify the object as the target or potential target. The object may be tagged for further analysis prior to being added to the target list, or be tagged a non-target or be tagged to be ignored. The further analysis may be performed using the same or additional data such that the drone  202 ,  1200  is made to collect additional data for analysis. In this way a predictive first analysis may be performed that requires fewer analysis resources and reduced analysis time. The predictive first analysis can be used to optimize one or more resources of the drone  202 ,  1200  and only commit the resources to objects that are predicted to be targets. The predictive first analysis may be followed by a second analysis or a series of analysis prior to being added, or not, to the target list. An object may be added to the target list based on one, two, or any number of analysis cycles consistent with mission rules. The target list may be verified prior to or after a spray vector has been calculated. 
     The targeting system  842  adds the target to the target list to be sprayed and the target so added to the target list is identified to the spray vector calculation subsystem within the targeting system  842 . The flagged target is added to the target list so that the target&#39;s desired contact area and spray center point may be computed. The target list may comprise one or more GPS/RTK coordinates and one or more heights above the ground. 
     The targeting system  842  may receive input data from various data sources, and analyze the data to identify, select, and prioritize targets, track real-time or near-real-time relative target location, calculate and converge on spraying solutions, and control drone spraying. The targeting system  842  may receive data from the cameras  630  and/or the sensor units  606 ,  616 . The data may include drone location data, drone movement vectors, drone vibration data, weather data, target images, distance/range data, infrared data, and any other sensor data described herein. The drone  202 ,  1200  may include a rules data store which may include identification rules for plants, pests or other target types. The rules data store may include target selection and target priority rules. The rules data store may include spraying rules, and other chemical application rules specific to the mission, the chemical(s) being applied, the target, and any other camera/sensor data input. 
     In one aspect, the drone  202 ,  1200  may identify a desired contact area for the treatment to be applied to the target. The desired contact area may be a portion of the target based on target-specific characteristics such as those used for verification or may be a result of the verification step. The desired contact area may be determined at any point in the process. The contact area may be any particular shape or size relative to the target. The target area may be determined based on the mission objectives and parameters. For example, if the mission is to spray weeds with a herbicide, a contact area for a targeted weed may include a portion of a leaf, an entire leaf, a group of leaves, stem, root(s), or the entire plant. In another aspect, the base station  204  may identify the desired contact area for the drone  202 ,  1200  to treat the target. 
     An object detection may involve an analysis of the image data, sensor data, etc., to detect one or more objects that may be targets within a proximity of the drone  202 ,  1200  based on the mission rules. The target identification may involve comparing object data and characteristics to a target data base  810  or target identification rules to recognize desired targets and distinguish targets from non-targets. The target identification rules may be based on one or more GPS/RTK coordinates, relative locations to other objects, and/or visual characteristics. For example, the object may be detected and compared to the onboard plant database  824  to identify the object as a weed or pest and distinguish the object from a non-target desirable plant and/or a weed or pest that has already been treated. Further, the identified weed may be added to the target list for verification or tagged for future treatment depending on the mission rules. If the object detected is not matched to the onboard plant database, the data may be relayed to the base station  204  or the mission command center  932  for further analysis with a more extensive plant database. The onboard plant database of each drone  202  may be subsequently updated with the newly identified plant in order to facilitate more efficient determination of the plant by other drones  202 ,  1200 . 
     For example, as shown in  FIG. 27A , an initial image  2700  may be captured by the data collection system and processed by the object detection.  FIG. 27B  shows the image  2702  following the processing. The object detection has identified crop plants  2704  (e.g. surrounded by white boxes) and identified weeds  2706  (e.g. surrounded by black boxes) and surrounded those identified plants  2704  and weeds  2706  with one or more bounding boxes that have been calculated. Based on the bounding box, a center point may be determined as described herein and may correspond to the spray target area. In some aspects as described herein, only weeds  2706  of a sufficient size may be targeted whereas weeds  2708  below a certain size may not be targeted until the weed  2708  has grown to a sufficient size. 
     Before or after the drone  202 ,  1200  is within the range of a target, as defined by the mission rules, data from additional sources may be used including image data or sensor data from the drone  202 ,  1200  and/or other drones  202 ,  1200 . The drone  202 ,  1200  may also use approximation techniques if a particular camera  630  and/or sensor  606 ,  612  is not available or has failed. For example, if the exact target range is unknown, the targeting system  842  may use onboard calculations to approximate range data using estimated target height relative to target surroundings to calculate the distance of the target from the drone  202 ,  1200 . Accordingly, the image data and the sensor data and the approximations may be used by the targeting system  842  to confirm the location of the target and resolve the desired spray vector. 
     The targeting system  842  may include a target verification subsystem. If the targeting system  842  determines the object is a target or likely to be a target, the object is flagged as such and sent to the target verification subsystem. The target verification subsystem uses the mission rules and the object data from the data collection system  848  and algorithms to verify that the object is a target to be sprayed. The target verification subsystem is used as a final check that the object is to be sprayed according to the mission rules. The target verification subsystem may add the object to the target list or flag the object as the obstacle, or a non-target, or the object to be ignored, or as a target to be included in another mission. 
     The target verification may determine if the identified potential target is to be sprayed by the drone  202 ,  1200 . The target verification may involve additional analyses of the same images/sensor data already collected by the drone  202 ,  1200  and/or another drones  202 ,  1200 . The target verification may involve analyses of additional image data and/or sensor data. The target verification rules may be based on the configuration of the drone  202 ,  1200 , the resources of the drone  202 ,  1200  remaining at the time of verification, the mission priorities in effect at the time, and/or other criteria. The verification rules may also involve the probability that the target has been accurately identified. The verification rules may also involve the confidence level that the spray will reach the target in a sufficient dosage. The verification rules may also involve the probability of an over spray or an under spray. The verification rules may also involve the probability that non-targets may be sprayed as the spray vector hits the desired contact area. The object detection, the target identification, the target verification, and the determination of the desired contact area may be performed in a single step or in two or more separate steps or as a series of iterations of one or more steps. 
     In some aspects, the target verification may comprise image registration and/or geocoordinate registration whereby the previously captured sensor data of the target and/or geocoordinates may be saved and compared to the newly captured sensor data. For example, a matching two photos of the same target plant at different times, which might be slightly different (e.g. different angle, different position in photo, moved by wind, etc.). The image registration and/or geocoordinate registration may ensure that multiple passes do not spray the same target plant more than once or may be used to determine a health of the target plant in order to determine if a more effective treatment may be necessary. 
     The techniques for object detection, target identification and verification, may also be applied to gathering data for target prioritization such that the same or similar image and sensor data is used. Examples of the target prioritization include rules that prioritize the targets that are dropping or ready to drop seeds, targets that are larger, targets that are closer to or further away from non-targets, targets of a particular variety over another variety, targets with desirable orientation relative to the drone  202 ,  1200 , targets with higher identification confidence, targets with higher hit confidence, etc., or any combination thereof. 
     The targeting system  842  may include a target prioritization subsystem. The prioritization subsystem processes the verified target list to optimize the grouping and sequence of the verified target list according to the mission rules. The prioritization subsystem processes the target list including target information and target tracking data, to determine if there are targets that should be grouped together and the relative order or sequence in which the targets should be sprayed. If the drone  202 ,  1200  includes multiple spray nozzles  520 , the prioritization subsystem assigns a specific nozzle  520  to the target or the group of targets. If the prioritization subsystem creates a target group, then spray vector computation subsystem may give the target group a single, contact area, center point, and/or spray vector for that target group. The prioritization subsystem acts as a mission optimization frontend for the spray vector computation subsystem by adjusting the target list received by the prioritization subsystem to create a second target list that is the input to the spray vector computation subsystem. Thus, the spray vector computation subsystem achieves resource efficiencies by having a predetermined target order that optimizes the timing of each spray relative to the time and resources required for each spray vector calculation. The spray vector computation subsystem also achieves resource efficiencies by calculating fewer spray vectors because of the target groupings. 
     The prioritization subsystem may potentially reformulate the second target list based updated information from the data collection system  848 . For example, the data collection system  848  may detect target movement that changes the relative location of the target such that the target sequence is adjusted or the target is removed from the target list such as when the target has moved or is expected to move out of range as dictated by the mission rules. 
     In one aspect, the data gathered by the drone  202 ,  1200  regarding the environmental and other local conditions may be used to modify the prioritization rules for the drone  202 ,  1200  or one or more drones  202 ,  1200  by relaying the data from to the base station  204  or directly to the one or more drones  202 ,  1200 . For example, a local micro topography and wind data may be used to modify the prioritization rules. As a further example, one or more non-target plant characteristics in a row or section may create a wind pattern that may be different from other rows or sections in that shorter or less dense non-target plants may not block the wind. In such a circumstance the spray vector may be affected to a greater degree in these rows compared to other rows or sections. Such variations may be accommodated by modifying the prioritization rules and/or modifying the spray vector algorithm. Further the conditions of the drone  202 ,  1200  and the components  600  may be used to modify the prioritization such as a life of the battery  618 , the amount of spray remaining in the canister  406 , the achievable spray pressure, etc. Further, a complete or partial failure of a drone component  600  may be the basis for modifying the prioritization rules or the spray vector algorithm. For example, a lower than expected tank pressure resulting from a pump problem or a leak in the spraying system  500  may cause a modification. Similarly, a partial or total failure of the camera  630  and/or the sensors  606  may cause the prioritization rule modification. 
     In one aspect, the drone  202 ,  1200  may use the image data and/or the sensor data to determine how effectively the target was sprayed. For example, the image data and/or the sensor data may determine that the moisture on the target plant after being sprayed is less than desired or more than desired. The determination may be used to modify the prioritization rules and/or the spray vector algorithm. 
     For each target to be sprayed, the targeting system  842  may calculate a spray vector. The spray vector may include a specific position and orientation for the spray nozzle  520 , a precise spraying time and duration, a spray geometry, a spray pressure, a distance between the spray nozzle  520  and the desired contact point at the spray time, a time required for the spray to travel from the spray nozzle  520  to the desired application area, etc. The spray vector may be calculated to aim a tip of the spray nozzle  520  and a tip vector of the nozzle  520  to spray a specific spray contact area such as a portion of the target to optimize the treatment objectives within the mission rules. For example, the spray vector may aim for a base of the target plant or a leafy area, or the entire target, or the head or the body or another portion of the target. The spray vector calculation may include a number of factors including a relative speed and heading of the drone  202 , an inertia of the drone  202 ,  1200  and the spray nozzle  520 , a relative stability and vibration of the drone  202 ,  1200  and the spray nozzle  520 , a time lag from the spray command to the spray initiation, one or more environmental conditions such as humidity, wind, and rain, a dampness or dryness of the target, the size of the target and the identified contact area, an aerodynamic drag of the spray and wind effects on the spray travel, an effect of gravity, the size of the desired target contact area, one or more available spray pressure(s) and geometry(s), a velocity of the spray leaving the spray nozzle  520 , an anticipated movement of the target, a proximity of non-targets, and any other factor relevant to mission success. 
     The number of factors used in calculating a spray vector affects the complexity of the calculation and thus the computing resources  602  and time required for the calculation. Similarly, each factor may be a variable that introduces uncertainty in the spray vector calculation. The uncertainty may be used as a probability of a successful spray and the probability may be used to modify the spray vector to increase the probability of a successful spray. The probability calculation may include such results as the probability of affecting a non-target, or not applying the desired amount of spray to the desired contact area. In response to probability calculations the drone  202 ,  1200  may for example, increased or decrease the number or the boundaries of desired contact areas. The probability calculations may affect the mission rules or target priorities. The probability calculations may be combined with ongoing or intermittent scoring of how successful past sprays have been. In some aspects, the drone  202 ,  1200  may adjust the velocity of the drone  202 ,  1200  by reducing power to the motors  610  so that the spray may be more accurate and thereby more successful. 
     The targeting system  842  may include a first predictive analysis subsystem  822  directed to implementing a triage type analysis to quickly and efficiently disposition objects that can be identified with fewer computation resources  602  while flagging those objects that require more computational resources  602  to distinguish for further processing. The first predictive analysis subsystem  822  may make object type determinations using fewer computational resources  602  and less computational time where possible. One purpose of the first predictive analysis subsystem  822  may be to use less computational time and resources  602  to quickly and efficiently distinguish targets and potential targets from everything else and flag those objects to the target identification system for further processing. The first predictive analysis subsystem  822  may be to use less computational time and resources of the processor  602  to quickly and efficiently distinguish objects that should be ignored from everything else and flag those objects as objects to be ignored. The first predictive analysis subsystem  822  may be to use less computational time and resources to quickly and efficiently distinguish obstacles from everything else and flag those objects to the drone navigation system  608 . The first predictive analysis subsystem  822  may be to use less computational time and resources of the processor  602  to quickly and efficiently distinguish non-targets that are not located near objects identified as targets or potential targets or objects that require further processing and are less likely to be affected by overspray and therefore not included in an overspray calculation. 
     The targeting system  842  may include a second analysis subsystem. The second analysis subsystem may be designed to perform object type determinations on objects not dispositioned by the first object identification subsystem. The second analysis subsystem may be designed to use additional data and/or more computational resources to perform object type determinations than were allocated to the object by the first object identification subsystem. 
     As previously described with regard to the first and the second analysis subsystem, this may be extended to the targeting system  842  having a series of object identification subsystems each directed to using greater computational resources and more computational time than the preceding object identification subsystem. The series of object identification subsystems may allow the targeting system  842  to manage computational time and resources to only apply the greatest computational time and resources to those objects that are not dispositioned using less computational time and resources. Each object identification subsystem may provide processed data and computational results to the next object identification subsystem until the mission rules are satisfied regarding object disposition. This allows the targeting system  842  to direct the data collection system  848  to gather more data if needed based on the mission rules and to optimize computational resources and power resources to be used as needed. The mission rules may specify that not all objects need to be identified and dispositioned and may contain rules that allows the data collection system  848  or the targeting system  842  to ignore objects or classes of objects while only gathering minimum data and performing little or no object dispositioning. 
     In some aspects, the targeting system  842  may use a target tracking method to determine precisely, or to estimate, the location of the target and/or the desired target contact area as a factor to be used in calculating the spray vector. The target tracking method may include a single calculation step, for example by maintaining constant heading and velocity and relative altitude above ground for the drone. A single calculation can provide the target coordinates in relation to the drone for each point of time during the drone approach. The target tracking method may include multiple calculation steps or may include an iterative and/or recursive calculation process. For targets that are moving or may move, a dynamic target tracking method may be used. The dynamic target tracking method may be used for the target having for example a stationary base but where the desired contact area is moving, for example a wind-blown plant. The dynamic target tracking method may be used where the entire target may move such as an insect or small animal. 
     The targeting system  842  may include a moving target computation subsystem. The data collection system  848  determines if the object is a moving object and flags the object as a moving object for the targeting system  842  for processing by the moving target computation subsystem. The target identification subsystem and target verification subsystem may also flag a target as the type of target that may move. The data collection system  848  continues to observe the moving target or potentially moving target to provide target movement data to the moving target computation subsystem. The moving target computation subsystem calculates an expected target trajectory and/or time sequenced series of target location coordinates using target movement data, target movement algorithms and other target data. The moving target computation subsystem may provide expected target trajectory or coordinates to the prioritization subsystem. The moving target is processed by the moving target computation subsystem to provide input to the spray vector computation subsystem. The moving target computation subsystem may be designed to calculate a target location uncertainty input to the spray vector computation subsystem. The moving target computation subsystem or a separate similar subsystem may be used to track moving or potentially moving non-target objects or obstacles. 
     In some aspects, the dynamic target tracking method may include factors including target movement patterns including frequency, duration, direction, and/or magnitude of acceleration and/or changes in direction. The target movement pattern may be continuous in a straight or curved line and may include hopping or jumping movement. The dynamic target tracking method may include predictive algorithms based on the specific target characteristics and extrapolations of observed movements. The dynamic target tracking method may include predictive algorithms based on information in a target database and/or based on recent observations of the same or similar types of targets. The dynamic target tracking method may anticipate future changes in target course or speed, based on factors such as upcoming topography changes and/or obstructions in the target&#39;s anticipated path. 
       FIGS. 30A and 30B  demonstrate a new spray vector being calculated and executed because of a target movement. In this example, the target  3004  may have been stationary when a first spray vector  3002  was calculated as shown in  FIG. 30A . The target  3004  began moving or is anticipated to begin moving as per pest vector  3006  and changed an acceptable spray vector  3002  into an unacceptable spray vector. When the target  3004  and the drone  202  are moving and/or are anticipated to be moving, dynamic target tracking may be required to generate or update a spraying solution, and additional variables may increase the complexity and uncertainty of the spray vector calculation. For example, dynamic target tracking may be used to determine the current velocity and direction of travel of both the drone  202  and the target  3004 , and those velocities and directions may be used to calculate an anticipated velocity and a direction of target travel in order to calculate the new spray vector  3008  and/or a new direction of travel for the drone  202  as shown in  FIG. 30B . 
     In some cases, the targeting system  842  may assume that the target  3004  may continue along a linear course with the same current velocity and direction, and if the target  3004  is currently moving along a non-linear path or movement pattern, then the targeting system  842  may assume the same non-linear path or movement pattern. As noted above, when performing dynamical tracking on a moving target  3004 , the determination of a spray vector (e.g., predicted future coordinates at a future spraying time) also may take into account the anticipated time to engage the motor to position and orient the drone  202 ,  1200  at the correct spraying point, as well as the anticipated time lag for a fired projectile to reach the target. Note that a movement pattern may include stopping, starting, and moving in three dimensions such as jumping. Note also that the target contact area may be recalculated intermittently or continuously and that a target may be assigned multiple target contact areas. 
     To accommodate the target uncertainty resulting from the leaf movement indications, namely the uncertainty in the target area and the center point at the spray time, the targeting system  842  may calculate a spray vector that requires the drone spray position component and the spray nozzle tip position component to be closer to the target than it may otherwise be if the target were stationary. Additionally, the targeting system  842  may calculate a spray vector that changes the spray vector geometric component to be wider than it may otherwise be if the target were stationary. 
     The spray vector calculation for a target which is partially fixed and partially moving, the targeting system  842  can perform a similar spray vector calculation the entire target may move such as an insect or small mammal with the set of target spray conditions being defined by the potentially moving target and being calculated based on the same, different or additional target characteristics as discussed herein. The spray vector calculation for the target  3004  that can be applied to multiple targets simultaneously or near simultaneously as discussed herein and in doing so may continuously perform dynamic tracking for all targets selected and prioritized. 
     In some aspect, the targeting system  842  may determine the spray vector by considering a travel time for the drone  202 ,  1200  to reach the desired spray location with a heading and velocity and/or to mechanically move the selected spray nozzle  520  from a preceding position to the desired spray point from which the spray is to be initiated. In some aspect, the targeting system  842  may determine the spray vector by considering the time required for the target to move to the desired spray location. 
     In one aspect, the drone  202 ,  1200  comprises one or more steps of detecting multiple objects, identifying, verifying, and/or prioritizing multiple targets, calculating multiple spray vectors, determining the success of multiple sprays and using the spray success determinations as an input to subsequent spray vector calculations. One skilled in the art will understand that these systems, subsystems or portions thereof used to perform these steps, can be combined, used in a different sequence than described herein. In some aspects, simplified and in some implementations omitted to achieve a less complex and/or less resource intensive design. 
     The targeting system  842  includes a target contact area computation subsystem. The target contact area computation subsystem includes the contact area center point computation subsystem. The contact area computation subsystem uses mission rules, spray chemical composition, target data from the data collection system  848  and target identification data to calculate the size and/or orientation of the target relative to the drone  202 ,  1200  and/or one or more possible positions of the nozzle  520 . The drone  202 ,  1200  and/or one or more possible positions of the nozzle  520  may be estimated at the estimated time of the spray command being initiated using data from the drone navigation system  608 , and the drone mechanical status system (not shown) and the target location data. The target location data may be known from the target data collection subsystem or estimated by the moving target computation subsystem as previously described. The contact area computation subsystem may calculate the contact area to define one or more geometric 2-D or 3-D boundaries. The contact area may be calculated to optimize the mission objectives, for example, to kill an identified weed or an identified pest with the smallest reasonable amount of spray necessary. For example, if the target is a plant, the contact area may include one or more leaves, a stalk, a base, an area around the base, or any combination thereof. The contact area may be calculated to maximize the sprays absorption into the target or into the earth surrounding the target. The contact area may be calculated to concentrate the spray on the body of a pest or an area surrounding the pest but including the pest. The contact area may be calculated to concentrate the spray on a head, face or eye of a pest such as a mouse or other unwanted living creature. The contact area geometry may be an input to the spray vector computation subsystem. 
     The contact area computation subsystem may calculate a contact area center point using a center point computation subsystem. The calculated contact area and center point are reference points to be used by the spray vector computation subsystem for calculating the spray vector for the target. The center point may be calculated based on the contact area geometry and spray vector variables selected to simplify the spray vector calculation. For example, the center point may be calculated based on the spray geometry and the estimated target range at the time the spray command is estimated to be issued. The center point may provide an aiming reference point used by the spray vector computation subsystem for calculating the spray vector and factoring the uncertainties and probabilities of the moving drone  202 ,  1200  to aiming the nozzle  520  at the stationary or moving target in the environment that includes wind data and non-targets to be avoided. For example, the spray vector computation subsystem may center the centerline of the spray geometry on the target center point. 
     The contact area computation subsystem may calculate an amount of spray to be deposited on or within the contact area to achieve the mission objectives for the specific target. For example, the amount of spray to be deposited may be determined by the desired effect on the target, the target characteristics, and the chemical composition of the spray, and current environmental conditions, such as wind or moisture. 
     The targeting system  842  may include a spray vector computation subsystem. The spray vector computation subsystem may calculate the spray vector command set. The spray vector command set may be a set of commands issued to the drone to instruct the drone  202 ,  1200  to execute the spraying of the individual target or the cluster targets. Each spraying event may have an associated spray vector command set. The spray vector command set may include at least the positioning of the drone  202 ,  1200  and at least one spray nozzle  520  and may include the timing of the spray on command to initiate the spray and the timing of the spray off command to stop the spray. The spray vector command set may also include commands to change the position of drone  202 ,  1200  and or the spray nozzle  520  on the drone  202 ,  1200  to achieve the desired distance and orientation of the nozzle tip  520  relative to the target, the spray geometry, and may also include the movement of the spray nozzle  520  and drone  202 ,  1200  before, during, and/or after the spraying process. 
     The spray vector computation subsystem may use a variety of factors, inputs and calculations to determine the spray vector for each spraying event in accordance with the mission rules. These factors, inputs and calculations may include the contact area geometry and the center point, the motion vector of the nozzle tip  520  and the expected spray momentum and inertia, the uncertainties and probabilities introduced by the movement and vibration of the drone  202 ,  1200 , the localized wind effects, the spray chemical characteristics including its specific weight, dispersion characteristics, and anticipated aerodynamic drag, gravitational affects, and target movement trajectory, probabilities and uncertainties (if any). The spray vector computation subsystem may use a continuous best fit analysis to continually refine the spray vector command set and/or contact area(s) boundaries and center points. The spray vector computation subsystem may calculate the spray vector command set once, twice, or multiple times based on a predetermined schedule or based on a material change in one of the factors used to calculate the contact area or the spray vector. 
     The targeting system  842  may provide to the spray vector computation subsystem data and rules regarding non-targets. The spray vector that results in the spraying of non-targets may be undesirable or extremely undesirable. Thus, non-target spray avoidance may be a high priority even to the point of the spray vector computation subsystem flagging the target as off-limits for this mission and thus not issuing a set of spray vector commands. The proximity of the non-target to the target may also affect the calculation of the desired contact area(s) for the target. The contact area(s) may be calculated to be located less proximate to the non-targets even though the resulting contact area(s) may be less desirable and more difficult to hit with the pray vector. As before, a less optimum contact area as related to a volume intersection (hereinafter referred to as the V-INT) as discussed herein may result in the target being flagged as off limits and not to be sprayed at this time under these conditions. For example, the target flagged as off limits for this mission may be sprayed successfully during a future mission when the wind may be less of a factor or other factors may be less of an issue regarding overspray. The overspray of non-targets may be such a priority that the same or greater methods and rigor as described herein to achieve a successful spray may be used to avoid an overspray of a non-target. 
     The targeting system  842  may include a non-target avoidance subsystem that can override the spray vector computation subsystem. In the same or similar way a target contact area may be calculated as an input to the spray vector computation subsystem. The non-target avoidance subsystem may calculate an avoidance area with one or more avoidance boundaries and/or a probability of avoidance of the non-targets referred to herein as P avoid . The spray vector computation subsystem may use a P avoid  calculation is a similar way it uses a P spray  calculation to refine and finalize the spray vector for a target and the resulting spray vector command set. A P avoid  may have an acceptable range of values similar to the P spray  and may be designated by the mission rules as a more important, less important or equally important result relative to P spray  such that P avoid  may be an overriding mission objective. 
     To execute the spray process, the spray vector computation subsystem may generate commands that the targeting system  842  sends to the drone navigation system  608  and the drone mechanical system  650  to adjust the drone&#39;s pre-spray state to the desired spray state including the current drone velocity and orientation relative to the target and to direct servo motors  610  to drive the spray arm  1222  orientation and the spray nozzle tip  520  orientation, including adjusting the nozzle tip  520  to control the spray geometry in order to match calculated spray vector components. 
     These mechanical and navigational adjustments may have a certain amount of time delay to reach the commanded state. The spray vector may be updated intermittently or continuously during this time delay and the orientation commands may be intermittently or continuously updated so that the drone  202 ,  1200  may be continuously aimed at the most recent spray vector target point. The target identification, verification, and prioritization subsystems may continue to update their respective inputs to the spray vector computation subsystem and the spray vector calculation may be updated based on new camera and/or new sensor data, and other relevant data received during the time delay for the drone state change. 
     The targeting system  842  initiates a spray on and spray off command to the spray nozzle vector control system to execute the spray and control spraying by the drone  202 ,  1200 . The entire process may be conducted simultaneously or near simultaneously for the next prioritized targets using different spray nozzles  520  or the same spray nozzle  520 . 
     The targeting system  842  may include a success determination subsystem. The success determination subsystem may use post-spray target data gathered by the target data acquisition system to provide feedback to the targeting system  842  regarding the effectiveness of the spray relative to the spray objectives. For example, the target data acquisition system may use the cameras  630  and/or the sensors  606 ,  612  to measure how much spray was deposited on or within a target&#39;s desired contact area. This measure of success may be used to calibrate and adjust certain targeting system  842  calculations for subsequent spray vector inputs and calculations and as inputs to the mission rules and mission planning process. If the spray or a sample of sprays is measured to be not successful within a first predetermined success threshold, then the targeting system  842  may be configured to adjust one or more targeting computations in real-time, or near real-time, to increase the probability of success for the upcoming prioritized targets. If the spray or a sample of sprays is measured to be not successful within a second predetermined success threshold, then these targets that were not successfully sprayed may be flagged by the targeting system  842  to be resubmitted to the target prioritization subsystem for immediate spraying or spraying at a future point in the current mission by the same or a different drone, or be flagged for treatment during a future mission. 
     The success determination subsystem may report to the targeting system  842  that a target or group of targets or an entire area has been successfully treated and are to be excluded from target selection during future missions. The success determination subsystem may report to the targeting system  842  a success rate for an area or a type of target or a certain set of mission rules. 
     Certain aspects of the present disclosure relate to techniques for determining the changing target range, including angular distance, as the drone approaches targets. As the drone  202 ,  1200  travels the projected spray points of application on the target may become closer and closer and the probability of a successful spray may increase continuously until a maximum probability of success is reached when the spray vector&#39;s projected spray geometry intersects the contact area to the maximum extent available to the spray vector calculation. A theoretical projected spray geometry intersection with the contact area may be a prediction of the volume of spray that intersects with the contact area and can be considered as the spray coverage over or within the 2-D or 3-D contact area of a target. This volume-intersection is referred to herein as V-INT. The mission rules may define a maximum and minimum volume of spray to be applied to a given target surface area for a given target type. The calculated V-INT may be express as microliters per square, millimeter or microliters per cubic millimeter, or any other convenient reference units. 
     The desired maximum V-INT and minimum V-INT may be established by the mission rules as a V-INT range. The mission rules may establish different V-INT ranges. For example, the mission rules may establish a different V-INT range for different factors such as mission objectives, different types of targets, different spray chemical compositions, different proximities to non-targets at the spray time, different environmental conditions, etc. The mission rules may establish a different V-INT range for a single factor or a combination of factors. The mission rules may provide one or more algorithms to allow the targeting system  842  to determine different V-INT ranges prior to a spray vector calculation. 
     Once a spray vector is calculated that provides a V-INT that is within the V-INT range for the particular target, then the targeting system  842  may issue a set of spray vector commands that direct the drone to configure itself according to the spray vector and execute the spray vector to spray the target. In some aspects, the targeting system  842  may issue a complete set of spray vector commands after a single spray vector calculation. In another aspect, the targeting system  842  may issue individual spray vector commands or a subset of spray vector commands to incrementally create a complete set of spray vector commands. The individual or subset of spray vector commands may be issued, or may be updated, during an iterative series of two or more spray vector calculations for a particular target. A complete set of spray vector commands may be limited to only the spray on and spray off times and the drone characteristics that need to be adjusted from the most recent spray vector commands. For example, the orientation of the drone  202 ,  1200  and the spray nozzle  520  may not need to be adjusted from the last spray sequence to the next spray sequence. In another example, a complete set of spray vector commands may be limited to a spray on time and a spray duration. 
     In one aspect, a new spray vector may be calculated and executed because of a drift of the drone  202 ,  1200  detected by the drone navigation system  608 . In this example, the drone  202 ,  1200  may be blown by a gust of wind that changes an acceptable spray vector into an unacceptable spray vector. For example, a heading, velocity and/or orientation of the drone  202 ,  1200  expected just prior to the spray on command being executed may change to a material extent such that the spray vector may be recalculated resulting in a new set of spray vector commands controlling position of the drone  202 ,  1200  via the drone navigation system  608 . This is an example of the calculated spray vector drifting out of the contact area from the drone drift. This drifting out of the contact area represents the spray vector starting from a higher V-INT but moving to a lower V-INT and then to a V-INT that is outside the V-INT range for this target. Another example of the spray vector being recalculated and a new set of spray vector commands being generated and executed by the processor  602  may result in the mechanical system  650  moving the entire drone  202 ,  1200 , or the spray nozzle  520  or both. 
     In another aspect, a new spray vector may be calculated and executed because of target movement. In this example, the target may have been stationary when the first spray vector was calculated but the target began moving or is anticipated to begin moving and changed the acceptable spray vector into the unacceptable spray vector. When the target and the drone are moving and/or are anticipated to be moving, dynamic target tracking may be required to generate or update a spraying solution, and additional variables may increase the complexity and uncertainty of the spray vector calculation. For example, dynamic target tracking may be used to determine the current velocity and direction of travel of both the drone and the target, and that data may be used to calculate the anticipated velocity and direction of target travel. In some cases, the targeting system  842  may assume that the target may continue along a linear course with the same current velocity and direction, and if the target is currently moving along a non-linear path or movement pattern, then the targeting system  842  may assume the same non-linear path or movement pattern. As noted above, when performing dynamical tracking on a moving target, the determination of the spray vector (e.g., predicted future coordinates at a future spraying time) also may take into account the anticipated time to engage the motor to position and orient the drone  202 ,  1200  at the correct spraying point, as well as the anticipated time lag for the fired projectile to reach the target. Note that a target movement pattern may include stopping, starting, and/or moving in three dimensions such as jumping. Note also that the target contact area may be recalculated intermittently or continuously and that a target may be assigned multiple target contact areas. 
       FIG. 28  demonstrates three scenarios. A first scenario  2800  demonstrates the drone  202  on a mission. When the drone  202  passes over the field and determines which nozzle  2802  is to be triggered at a calculated time that the drone passes over  202  a weed  2804 . For clarity, only a single spray nozzle  520  having a single spray nozzle tip  520  is activated whereas the drone  202  may have multiple spray nozzles  520  and each spray nozzle  520  may have multiple spray nozzle tips  520  and any combination of these may be selected for use on a target. The targeting system  842  includes a set of initial conditions for the drone  202  which include for example an initial location and an initial directional velocity vector, the selected spray nozzle and the location on the drone  202 , the spray tip and the spray tip&#39;s initial location on the spray nozzle, an initial spray nozzle vector and an initial spray geometry. In this example, the targeting system  842  determines a spray vector that includes calculating a set of spray conditions that are different than the set of initial conditions. The set of spray conditions may be achieved by changing one or more of the initial conditions to spray conditions. 
     The targeting system  842  may include a set of target initial conditions which include target characteristics such as for example the target identification, target location, target size, and target contact area, and center point. 
     For example,  FIG. 29A  shows a drone field of view  2900  expected just prior to the spray on command provided by the cameras  630  and/or the sensors  606 . The field of view  2900  includes two targets  2902 ,  2904  within a target contact area and one non-target  2906 . In this example the targets  2902 ,  2904  do not move but the drone  202  is moving in a direction towards the bottom of the image  2900 . A position of a boom of spray nozzles  502  are shown on the image  2900 . As can be seen in  FIG. 29A , none of the spray nozzles  520  sufficiently correspond in position to the targets  2902 ,  2904  and are close to the non-target  2906 . 
     Turning to  FIG. 29B , the drone  202  has moved position causing the bounding boxes for the targets  2902 ,  2904  to be adjusted to account for an updated relative position of the weeds identified as the targets  2902 ,  2904  become more visible in the image  2950 . The spray vectors and center points may be recalculated in real-time to ensure the spray operation hits the correct locations on the targets  2902 ,  2904 . The spray nozzles  2908  are now aligned with a center point of target  2904  and sufficiently correspond with target  2902 . In this instance, these spray nozzles  2908  may be activated in order to treat these targets  2902 ,  2904 . 
     Returning to  FIG. 28 , a second scenario  2830  the drone  202  is shown during a spraying mission where the drone  202  is blown by a gust of wind  2834  in only two dimensions. The targeting system  842  has already determined that the spray vector. In this scenario, a new spray vector may be calculated and executed because of the drone drift detected by the drone navigation system  608 . In this example, the drone  202  may be blown by the gust of wind  2834  that changes an acceptable spray vector into an unacceptable spray vector. A recalculation may determine a new position relative to the weed  2804  and the adjustment has directed nozzle  2832  to be activated rather than nozzle  2802 . 
     The drone drift may be determined by defining a circle associated with the spray diameter. If the circle drifts outside of the target area, then the navigation system  608  may determine that the drone  202  has drifted. This drifting out of the target area represents the spray vector starting from a higher V-INT but moving to a lower V-INT and then to a V-INT that is outside the V-INT range for this target area. A new spray vector may be calculated for each of the nozzles  520  to determine which nozzle  520  can most effectively hit the target area. Once a nozzle  520  has been selected, the new spray vector determines the spray area required for the target area. One or more incremental movements may be determined by the drone navigation system  608  moving the drone  202 ,  1200  and/or the drone mechanical system  650  moving a drone component to a new position, according to the new spray vector command set. This repositioning back into the target area results from the newly calculated spray vector that is within the V-INT range for this target. Note also that drone  202 ,  1200  may move the entire drone  202 ,  1200 , or the spray nozzle  520  or both. 
     In a third scenario  2850 , the drone  202  may be blown by a gust of wind  2854  that moves the drone  202  in three dimensions. The drone  202  has moved laterally as well as tilted the drone  202  such that the spray vector is no longer straight down. In this instance, the targeting system may recalculate the relative nozzle position and turn on the nozzle  2852  that is most likely to hit the target. Also, in this instance, an orientation of the nozzle  2852  may be adjusted such that the spray is not directed perpendicular to the boom of the drone  202 . 
     In some aspects, a radius of the spray may be adjusted in order to correspond to the target area. For example, for a larger target area, the radius of the spray may be increased whereas for a smaller target area, the radius of the spray may be decreased. This increase or decrease may be accomplished by adjusting the properties of the nozzle  520  and/or may be adjusted by having the drone  202  fly higher or lower to the target area. 
     In one aspect, the target contact area may be a single point selected by the targeting system  842  from multiple candidate single points on the target. In another aspect, the target contact area may be a 2-dimensional or 3-dimensional geometric shape having one or more boundaries. The boundaries may be calculated by the targeting system  842  and may be calculated based in part on the expected relative orientation of the spray nozzle to the target at the time of spraying and the target geometry. Each different factor chosen by the mission rules to be included in the spray vector calculation may each be represented by a variable. Since the variables may each have an associated uncertainty and may be changing during the targeting and spraying process, the theoretical maximum probability P max  available for the mission may be a percentage less than or equal to 100%. The actual calculated probability that a spray vector deposits an acceptable V-INT for a given target contact area is P spray . P spray  may be compared to the acceptable range between P min  and P max . 
     The boundaries of each of the multiple contact areas may or may not overlap. The targeting system  842  may calculate one or more initial target contact areas as an input to the spray vector calculation subsystem, then calculate all or a portion of an initial spray vector, and then use the initial spray vector calculation to revise the one or more calculated target contact areas. This calculation of one or more target contact areas and spray vectors may be repeated in an iterative process and may be constrained by a predetermined minimum probability of a successful spray referred to as P min . Since the calculation of the actual probability P of a successful spray may be based upon the V-INT range, the probability, P, of a successful spray may vary as the center point of the spray vector geometry may be directed to different points on the contact area. For example, P may be greater if the spray vector is centered at the geometric center of the contact area and P may be smaller if the spray vector is centered at or near a boundary of the contact area. Thus, the contact area may be calculated as geometric contact points within and including the contact boundary with each contact point or the group of contact points having the associated P if the spray center point were to be aimed at that contact point or group of contact points. In one aspect, the contact area boundary points may be calculated as the contact points approximating the P min  so that all points within the boundary have the predicted P greater than or equal to the P min . In another aspect, the contact area boundary points may form a circle or ellipse or any other shape. If the contact area is calculated in 3 dimensions, the area boundary points may be in the form of a cone, a sphere, a cube or any other 3-dimensional shape. The spray vector geometry may be calculated in 3 dimensions. These 3 dimensions are a 2-dimensional shape that changes according to the third dimension of time which is the time required for the 2-dimensional spray shape to travel from the nozzle tip to the contact area. The 3-dimensional geometry may be mathematically calculated as a 2-dimentional shape traveling and changing in 2 dimensions over the distance and time from the nozzle tip to the contact points. The spray vector geometry may be calculated in 4 dimensions. The same 3 dimensions as for the 3-dimension geometry but including a 4th dimension representing the difference in time between at least one of the first microdroplets leaving the nozzle tip and at least one of a later microdroplets leaving the nozzle tip. The four-dimensional geometry may be mathematically calculated as a cloud emanating from the nozzle tip  520  and traveling toward the target and changing in three dimensions over the distance and time from the nozzle tip  520  to the contact points. Any of the spray vector geometries discussed herein may be combined with any of the contact area geometries discussed herein. 
     A spray vector having a center point outside of the boundary area may have the spray vector P that is less than the P min . If the spray vector defines a tight geometry such as a tight spray stream with a small geometric coverage area at the point of spray impact. For example, such a tight spray geometry may have a predicted impact point of about 10 millimeters in diameter. Conversely, a spray vector defining a broad geometry may have a predicted impact point of about 100 millimeters in diameter and result in a much greater geometric coverage area. Thus, a spray vector defining the broader spray geometry but having the spray center point outside of the boundary area, may still have the spray vector P that is equal to or greater than the P min . 
     In one aspect, the mission rules may define the P min  based on a fixed spray geometry. In another aspect, the mission rules may define the P min  based on the fixed spray geometry and include mission rules that direct the targeting system  842  to adjust the spray vector geometry according to the drones micro environment and target conditions to achieve the P spray  that is equal to or greater than the P min . The target spray geometry may be in the form of a circle, an ellipse or any other 2-dimensional shape appropriate for the mission objectives. 
     For example, when a target vector calculation includes motion of either the target or the drone, the boundary area may assume a more elongated shape in the direction of the movement in order to account for the additional targeting uncertainties caused by the movement of the drone or target. For example, for a horizontally moving target and/or horizontally moving drone system, the boundary area may be shaped like a horizontally-elongated ellipse. In any of these examples, the boundary area may be defined in terms of any convenient coordinate system with reference to the drone  202 ,  1200 , the base station  204 , and/or field  2300 . 
     The size and number of contact areas may be based on any combination of factors. Different factors may be introduced to increase P max  by reducing uncertainties while other factors may introduce uncertainties into the spray vector calculation and thus lower P max  for a given target. For example, the size of the contact area may be based on: the target size, a distance between the drone  202 ,  1200  and the target at spray time, an accuracy of the drone navigation system  608 , a precision which the drone  202 ,  1200  can move and aim the spray nozzle  520 , an accuracy with which the spray geometry can be controlled by movement of the nozzle tip  520 , the influence of the wind, the pressure of the spray and resulting time required for the spray to reach the target, the vibration level of the drone  202 ,  1200  during drone component movement  650 , the vibration level of the drone  202 ,  1200  arising from the drone movement or any other relevant factor selected by the mission rules for use by the targeting system  842  or any combination of these factors. For targets having a large P min  where the relevant uncertainties are fewer, the contact area boundary may be relatively small. For targets having a small P min  where the relevant uncertainties are greater, the contact area boundary may be relatively larger. 
     In some aspects, the targeting system  842  may have achieved the spray vector that results in the P spray  being greater than P min  but then may delay the execution of the spray vector command set by changing the spray on command time to allow a period of time that may result in an increased P spray . In another aspect, the targeting system  842  may have achieved the spray vector that results in the P spray  being greater than P min  but then may calculate second spray vector and command set for the same or a second spray nozzle to spray the same target a second time to further increase P spray . This second spray may be initiated by the targeting system  842  in response to factors that changed or arose while the first spray was being executed. 
     Turning to  FIG. 9 , a physical component architecture  900  of the treatment system  200  is shown. In this aspect, there may be one or more field scanning drones  902  and one or more field treatment drones  904 . The field scanning drones  902  may be aerial drones, as described with reference to  FIG. 5 , instrumented with one or more flight cameras  906 , a compass  908 , and a GPS  910 . In some aspects, the field scanning drone  902  may comprise one or more plant scanning cameras  912  separate from the flight cameras  906 . The field scanning drone  902  may traverse the field gathering field data in order to wirelessly relay the data to an on-site ground station management processing computer  914 . The field scanning drone  902  may dock with a battery/fuel management base station  920  in order to receive one or more new batteries and/or fuel. 
     In another aspect, the field treatment drones  904  may be a rolling treatment drone  1200  described in further detail below with reference to  FIGS. 12A and 12B . Similar to the field scanning drones  902 , the treatment drone  904  may comprise a compass  808  and a GPS  810 . The treatment drone  904  may comprise one or more obstacle cameras  924  for imaging a path of the treatment drone  904 . In some aspects, the treatment drone  904  may comprise one or more plant locating cameras  928 . The treatment drone  904  may also comprise a treatment payload  926  to treating particular pests. Although the aspect described is directed to the rolling treatment drone  1200 , other aspects may have a field treatment drone  904  be an aerial drone  902  as described in  FIG. 5 . Similar to the field scanning drone  902 , the field treatment drone  904  may dock with the battery/fuel management base station  920 . In addition to the battery/fuel management base station  920 , the treatment drone  904  may also dock with a drone pesticide management system base station  922 . The treatment drone  904  may also wirelessly communicate with the on-site ground station  914 . 
     The on-site ground station management processing computer  914  may comprise a weather station  916  and one or more artificial intelligence processing hardware  918 . The on-site ground station management processing computer  914  may communicate with the drones  902 ,  904  as well as the respective base stations  920 ,  922 . The processing computer  914  may also communicate via a wired network over an Internet  930  with a central farm/field job management server  932 . The job management server  932  may retrieve and store data to a central database server  934 . 
     Turning to  FIGS. 10, 10A and 10B , a precision AI conceptual structure  1000  is shown. A management infrastructure  1002  may comprise a mission planning module  1004  that provides mission data to a flight plan processing module  1006  that generates a flight plan for each drone  902 . The management infrastructure  1002  may receive an initiation input  1008 . A drone system  1010  may load the flight plan using a load flight plan module  1012  from the flight plan processing module  1006 . The flight plan may be divided into one or more flight plan sections at step  1014 . Each drone  902  may be given instructions to fly to a next location  1018  if it has received a start mission signal  1016 . As the drone  902  moves to the next location, the drone  902  may capture one or more images  1020  and may periodically transmit the one or more images to a buffer until the drone  902  determines that the drone  902  is in a rest state  1024  (e.g. landed at the base station  920 ). 
     When the drone  902  has landed at the base station  920 , one or more of the images may be retrieved by the base station  920  via network infrastructure  1026 . The images may have time and/or geocoded data associated with the image data processed  1028 . The images and time and/or geocoded data may then be passed to a pest detection artificial intelligence module  1030 . The received time and geocoded images may be stored in a RESTweb interface to a database at step  1034 . A decision  1036  on whether a pest is present may be determined by an AI algorithm, such as a semantic segmentation, plant phenotype detection, and/or spectral analysis. If the pest is detected, the pest detection AI module  1030  may respond  1038  with a pest status  1040  over the network infrastructure  1026  to the drone  902 . A reporting/presentation infrastructure  1042  may monitor the network infrastructure  1026  in order to determine locations of pests on a map using a mapping visualization monitor  1044 . 
     When the drone  902  receives a pest status message  1040  from the pest detection AI module  1030 , the drone  902  exits a waiting status  1046  and may act  1048  on the pest status message  1040 . The action  1048  may involve spraying or heating, etc. in order to treat the pests at the location. The drone  902  then determines if the flight plan has been completed at decision  1050 . If the flight plan is complete, the drone  1002  navigates and returns to the base station  920 ,  922  at step  1052 . Otherwise, the drone  902  process returns to fly to the next location at step  1018 . 
       FIG. 11A  shows an onboard 12-Volt electrical power distribution system  1100  for the drone  902 . The drone  902  may have a 48-Volt supply  1102 , a 12-Volt supply  1104 , and a ground  1128 . In this aspect, both of the supplies  1102 ,  1104  pass through a main emergency cut-off power switch  1106  that cuts off both supplies  1102 ,  1104  from other electrical components of the drone  902  when an emergency event is detected. The 12-Volt supply  1104  may supply power to a pressure pump power switch  1110  that may enable or disable power being supplied to a pneumatic pressure maintenance pump  1112  that may provide pressure for the sprayer  202 . 
     A processor power down push button  1108  may also be able to cut off the 12-Volt supply  1104  from the lower power electronic components. The lower power electronic components may comprise: a mission guidance communications and/or transportation controller  1114 , a plant detection time space correlation action/targeting AI processor  1116 , a multi-spectral camera  1118 , a real-time boom valve controller  1120 , one or more obstacle detection sensors  1122 , and a processor watchdog  1124 . A spray boom valve  1126  may be controlled by the real-time boom valve controller  1120  and may also receive power from the 12-Volt supply. The processor watchdog  1124  may monitor the electronic components for a lockup condition and when detected may reboot the drone  902 . 
     In another aspect shown in  FIG. 11B , an onboard 48-Volt electrical power distribution system  1150  is presented. In this particular aspect, the power distribution system  1150  may be for the field treatment drone  904 . Similar to the 12-Volt distribution system  1100 , the drone  904  may have the 48-Volt supply  1102 , the 12-Volt supply  1104 , and the ground  1128 . In this aspect, both of the supplies  1102 ,  1104  pass through a main emergency cut-off power switch  1106  that cuts off both supplies  1102 ,  1104  from other electrical components of the drone  904 . The electrical components of the drone  904  may comprise six drive motor controllers  1152  through  1162  that may rotate each of the six wheels  1206 . Each of these drive controllers  1152 - 1162  may be monitored by the watchdog  1124 . 
     Turning to  FIGS. 12A and 12B , the rolling treatment drone  1200  may be shown. In this aspect, the drone  1200  may comprise a plurality of wheels  1206  on both sides of a transportation cradle or housing  1208 . A camera housing  1202  may be mounted on a camera boom  1203  above the transportation cradle or housing  1208 . The camera boom  1203  may be coupled to a communication tower  1204 . The communication tower  1204  may be configured to communicate with the base station  204 . Located at a rear of the drone  1200  may be a one or more free rolling wheels  1220  that have a height generally above the ground. In this aspect, there are four free rolling wheels  1220 . Between each of the free rolling wheels  1220  may be a spray boom  1222  acting as an axle for each of the wheels  1220 . The spray boom  1222  may be supported by a pair of wing hinges  1216  and may have a nozzle impact guard  1226  in order to protect the nozzles  520  from damage. Mounted on the spray boom  1222  may be a valve block  1224  and a spray nozzle  520  between each of the free rolling wheels  1220 . Each valve block  1224  may control an amount of pesticide spray to each spray nozzle  520 . A pump  1210  may be mounted above the transportation cradle  1208  and may be connected to a hose  1214  to the valve blocks  1224 . The pump  1210  may supply pressure of the liquid pesticide to the valve blocks  1224 . 
     Turning to  FIG. 13 , an electronic system  1300  for the rolling drone  1200  is presented. The electronic system  1300  comprises a controller  1302  for mission guidance, communications, and/or transportation. As previously mentioned, the watchdog  1124  monitors the system  1300  for lockup and/or other anomalies. The controller  1302  may receive obstacle data from an obstacle sensing system  1304  and provide output to a drive motor controller  1306 . The controller  1302  may communicate with a plant detection time space correlation action/targeting AI processor  1332  that may receive one or more images from the multi-spectral camera  1118 . The AI processor  1332  may also send signals to a real-time boom valve controller  1120  that may initiate the pesticide spray from the spray boom valves  1224 . 
     For navigation, the controller  1302  may receive one or more GPS coordinates from a GPS receiver  1308  in communication with a GPS satellite constellation  1310 . The controller  1302  may also receive a signal from a real-time kinematic (RTK) radio  1312  from a GPS RTK base reference  1316  transmitting via another RTK radio  1314 . The navigation system  608  may receive this information in order to plan routing of the drone  1200  and/or calculate the relative GPS RTK coordinates of weeds/pests within the image data captured by the drone  202 ,  1200 . 
     The controller  1302  may also receive manual control instructions from a manual control radio  1318 . An operator manual remote control  1322  may transmit the manual control instructions via a manual control radio  1320  to be wirelessly received by the manual control radio  1318  in the drone  1200 . The controller  1302  may also wirelessly communicate with a mission control ground station  932  over a pair of mission control radios  1324 ,  1326  operating on the same frequency. In this aspect, the mission control ground station  932  may control the missions and the base station  204  may perform recharging and/or swapping drone batteries or spray. 
     As shown in  FIG. 14 , the field treatment system drone  1200  may have a pressurized pesticide mixture storage tank  1402  on board. A pressure sensor  1404  may measure a pressure within the tank  1402  and may regulate the pressure with a pneumatic pressure maintenance pump  1406 . For safety, a pressure relief valve  1408  may prevent excessive pressures within the tank  1402  or may permit a maintenance technician to release the pressure for maintenance. The tank  1402  may also have a drain valve  1410  located generally below the tank  1402  for draining the pesticide from the pesticide system  1400 . The tank  1402  may provide pesticide to the one or more solenoid valves  1224 . The solenoid valves  1224  may release the pesticide as a spray through one or more spray nozzles  520 . 
       FIG. 15  demonstrates a light indicator system  1500  for the rolling drone  1200 . As previously mentioned, the 48-Volt power supply  1102  and the 12-Volt power supply  1104  provide power through the emergency cutoff power switch  1106 . When the power switch  1106  is closed, a 12-V indicator light  1504  and a 48-V indicator light  1506  may be illuminated. When the pump power switch  1110  is closed, a pump power indicator light  1502  may be illuminated. Once the controller  1302  becomes operational, the controller  1302  may turn on a controller running indicator light  1508 . When the controller  1302  is prepared for a mission, the controller  1302  may turn on a controller missionable indicator light  1510 . The running indicator light  1508  and the missionable indicator light  1510  may be controlled by the watchdog  1124 . The watchdog may also control a band of indicator lights  1512  corresponding to an all stop/critical error state, a continued operation state, a spray system in action mode, and/or a system in mission mode. Each of these lights may be light emitting diodes (LEDs) or other type of illuminator. In other aspects, these indicator lights may be replaced with a display screen, such as an LCD, etc. 
     Turning to  FIGS. 16A to 16C , the transportation cradle or housing  1208  is shown in more detail. The transportation cradle or housing  1208  comprises a frame  1604  surrounding one or more batteries  1602 . The pesticide tank  1402  may be centrally located between a pair of batteries  1602 . 
     A drive and suspension system  1700  for the rolling drone  1200  is shown in  FIGS. 17A and 17B . In this aspect, the transportation cradle or housing  1208  may be supported by six wheels  1206  having a drive motor for each wheel  1206 . The wheels  1206  on each side may be coupled together with one or more connecting members  1708 . The connecting members  1708  may be coupled to one or more axles  1710  coupled to rotatable hubs  1706 . In this aspect, the suspension system  1700  comprises a pair of axles  1710 . The axles  1706  may be coupled to the transportation cradle or housing  1208  using one or more shocks  1702  and one or more leaf springs  1704 . 
     Turning to  FIG. 18 , an onboard electrical power supply system  1800  for the field treatment system  904  is shown. The power supply system  1800  comprises at least one 12-V battery charger  1802  supplied power from a 120-VAC plug  1706 . The chargers  1802  may provide electrical power to one or more 12-Volt deep-cycle marine batteries  1804 . The 12-V supply  1004  may be provided from one of the batteries  1804 . The 48-V supply  1002  may be provided from  4  of the batteries  1804  placed in series. 
       FIGS. 19, 19A and 19B  present a process  1900  generally executing on the electronic system  1300  for the rolling drone  1200 . The process  1900  may generally comprise a transportation control  1902 , a plant detection correlation targeting control  1904 , and/or a boom valve nozzle control  1906 . The transportation control  1902  may receive or calculate a ground speed  1908  of the rolling drone  1200  and may execute a spray mission  1910 . 
     If a spray missions  1910  has been executed, the targeting control  1904  may determine if the rolling drone  1200  is at an imaging location  1912 . If the rolling drone  1200  is at the imaging location  1912  and if the spray mission  1910  has been executed, then an imaging process  1914  is triggered. The imaging process  1914  triggers a multispectral camera system  630 , comprising one or more multispectral cameras, to capture image data. 
     When the image data has been captured, an extraction process  1918  may extract one or more frequency bands from the image data. A plant or pest detection location AI process  1920  may process the one or more frequency bands to determine a location of the plants. In another aspect, one or more geometric shapes of the pests may be used to determine a pest type. A combination of the frequency bands and the geometric shape identification may be used to further improve the determination of the pest type. 
     A current position of the nozzles  520  may be determined by process  1922  relative to the location of the rolling drone  1200 . A predictive process  1924  may then predict, based on a current time  1926 , a predicted time when the plant or pest will be under the nozzles  520 . 
     The nozzle control  1906  may then add the predicted time to a nozzle schedule  1928 . A nozzle scheduler process  1930  may receive the nozzle schedule  1928 , the current time  1926 , and any changes in the ground speed  1932 . If the ground speed  1932  has changed, then the nozzle schedule  1928  may be adjusted at step  1934 . If the current time  1926  has reached the predicted time on the nozzle schedule  1928  at step  1936 , then the nozzle valve may be turned on at step  1940 . If the current time  1926  has not reached the predicted time on the nozzle schedule  1928  at step  1936 , then the nozzle valve may be turned off at step  1938 . 
       FIG. 20  present a time coordination process flow  2000  for the sprayer system of the rolling drone  1200 . A mission, location, orientation, information from transportation, and/or communication system  2002  may provide data to a time/space correlation process  2004 . The time/space correlation process  2004  may work with the plant or pest identification location AI engine  1920  and may transmit an image time synchronization signal to the multispectral camera  630  and an action/targeting coordination process  1922 . The action/targeting coordination process  1922  may then instruct the schedule based boom valve control  1930  to either turn on the spray boom valve  1126  or turn off the spray boom valve  1126 . 
       FIG. 21  shows a steering system  2100  for the rolling drone  1200 . Many of the components for performing a steering action have been previously described with reference to  FIG. 13  and will not be repeated here. Each of the wheels  1206  and wheel motors  610  may be independently controlled by a drive motor controller  2104  that may rotate the wheels  1206  in a forward or a reverse direction. When the controller  1302  encounters a need to turn the rolling drone  1200 , such as to avoid an obstacle, the controller  1302  activates a turn motor  2102  in order to adjust an orientation of the wheels  1206 . In another aspect, the controller  1302  may instruct the wheels  1206  on the left side of the rolling drone  1200  to be driven in an opposite direction than the wheels  1206  on the right side of the rolling drone  1200  in order to effect an in-place rotation of the rolling drone  1200 . 
     Another aspect shown in  FIGS. 22A to 22C , an example aerial drone  202  has landed on a platform  204 A of the base station  204 . In  FIGS. 22A to 22C , the propellers  620  and the ends of the frame  1604  of the aerial drone  202  have been removed for clarity. A battery receiver  2220  may be aligned with a hole  2202  through the platform  204 A of the base station  204 . The platform  204 A may have a number of V-shaped or QR code guides (not shown) may be captured by the camera of the drone  202  in order for the drone to orient itself with respect to the platform  204 A. 
     Shown particularly in  FIGS. 22B and 22C , beneath the platform  204 A may be a generally cylindrical battery storage  2204 . Other aspects may have a differently shaped battery storage  2204 , such as a swivel arm or a lower-profile conveyor. The cylindrical battery storage  2204  may rotate about a central hub  2208  using an electric motor. Each of the storage compartments  2206  may receive the batteries (not shown) and may have an induction charger (not shown) or a contact charger (not shown) for charging the batteries. The base station controller  308  may determine which of the battery compartments  2206  has a battery that is the most charged. This battery compartment  2206  may be aligned with a scissor lift  2210  that retrieves the battery from the battery compartment  2206  and raises the battery to be deposited in the battery receiver  2220  of the drone  202 . Alternatively, the battery storage may be replaced by an in-place charger which may charge through an umbilical line and/or through contact with conductive plates or contacts contained in the landing gear or elsewhere on the drone  202 , which may drive current through the plates to recharge the batteries. 
     Turning to  FIG. 22D , another configuration for an aerial drone  902  is demonstrated. In this aspect, the aerial drone  902  comprises six or more propellers  620  above a housing  1208 , and a horizontal spray boom  1222  located below the housing  1208 . The horizontal spray boom  1222  may comprise between 12 and 24 spray nozzles  520 , each controlled individually with their respective valves. In this aspect, the number of spray nozzles  520  is 24. 
     Turning to  FIG. 22E , yet another configuration for an aerial drone  902  is demonstrated. In this aspect, a centrally located horizontal spray boom  1222  may be coupled at each end to an propeller arm  2236  resembling an extended H-configuration when viewed from the top view. Each of the propeller arms may comprise one or more propellers  620 . One or more wings and/or spoilers (not shown) may be included to ensure lift over the center section when travelling in a forward direction. In this aspect, each propeller arm  2236  is coupled to three propellers  620  for a total of six propellers  620 . In this confirmation, the effect of downwash from the propeller blades on the spray nozzles  520  may be reduced or eliminated. In this aspect, the spray boom  1222  may have up to 32 spray nozzles  520 . 
     Turning to  FIG. 23 , an example field  2300  is shown with the base station  204  located proximate to an edge or corner of the field  2300 . The base station  204  may comprise a charger  624  supplied with electrical power  2304  and/or a fuel storage and/or spray storage. When the aerial drone  902  lands at the base station  204 , the base station  204  may automatically begin charging the battery  508  using the charger  624 . In another aspect, the base station  204  may automatically swap the dead battery  508  with a fresh battery as previously described. The base station  204  may also have a receiver for receiver data from the aerial drone  902 . 
     The navigation system  608  of the aerial drone  902  may determine a flight path  2308  based, in part, on field data provided. For example, artificial intelligence framework  1920  may determine a crop geometry (e.g. row direction, spacing, width, etc.) and/or use computer vision to identify obstacles and/or may be supplemented by Geographic Information Systems (GIS) boundaries available from one or more public or private databases. The AI framework  1920  may interpret and process one or more of: (a) manual human input by drawing interior and exterior boundaries on a map of the area, and converting to GPS coordinates, (b) artificial intelligence detecting exterior and interior boundaries (e.g. based on crop orientation and geometry, spectral signatures of plant/dirt/non-organics, etc.), and/or (c) existing survey maps (either government or privately owned). The navigation system  608  may determine an optimal field of view  2306  based on one or more lens and camera parameters in combination with an altitude of the aerial drone  902 . For example, the field of view  2306  increases when the aerial drone  902  increases altitude but the image quality may degrade at higher altitudes. The navigation system  608  may determine the altitude where the image quality is sufficient in order to detect weeds  2320 ,  2322  present in the field  2300 . The image quality sufficient to detect weeds  2320 ,  2322  may be determined, at least in part, by an estimated size of weed based on growing conditions, a resolution of the camera(s)  630 , and/or weather conditions (e.g. a windy day may require slightly lower altitudes for an improved resolution). In other aspects, an optimal altitude may be determined at least in part by a canopy size, and/or one or more lighting conditions, such as determined by weather (e.g. cloudy vs sunny, foggy, rainy, etc.). In this aspect, the field of view may generally be a 12-ft by 12-ft area. 
     Once the field of view  2306  has been determined, the navigation system  608  may determine a number of passes necessary to pass at least once over the entire field  2300 . In this example, the path  2308  passes back and forth over the field  2300  seven times. If the field of view  2306  were reduced (by reducing altitude), the number of passes would increase. If the field of view  2306  were increased (by increasing altitude), the number of passes would decrease. The navigation system  608  may dynamically construct the path  2308  to survey the entire field  2300  using one or more boundary detection techniques. For example, if most of the field  2300  is in a specific color space (e.g. “green” for plants and “black” for dirt), the AI framework  1030  may determine a geometrically significant feature in another color space (e.g. “gray” for gravel road, or “blue” for pond, or “red” for tractor). The geometrically significant feature may form a boundary. 
     While the aerial drone  902  is passing over the field  2300 , the processor  602  may be processing image data from the cameras  806  using an artificial intelligence (AI) framework  1030  such as described herein in order to detect weeds  2320 ,  2322  and/or areas of undesirable growth and flag a weed area as a treatment area. When the processor  902  determines a weed  2320  is located on the planned path  2308 , the navigation system  608  may be instructed to land or lower or hover the aerial drone  902  within spraying (or treatment distance) once the aerial drone  902  reaches that point on the planned path  2308 . In another example, when the processor  902  determines a weed  2322  is not located on the planned path  2308 , the navigation system  608  may be instructed to deviate from the planned path  2308  by a certain threshold, which may be based on a proportion to row spacing and/or crop canopy size. In another aspect, the navigation system  608  may plan to land the aerial drone  902  at weeds  2322  not on the planned path  2308  during the return path  2324  to the base station  204 . 
     In another aspect, the processor  902  may determine the location of every weed  2320 ,  2322  and plan a treatment path using the plant detection artificial intelligence framework  1920  as previously described. In some aspects, the processor  902  may provide one or more GPS-RTK coordinates for each weed and/or pest, which may be used by subsequent treatment system(s) to create one or more missions, plan paths, and/or trigger spray nozzles based on sensor positioning data. The AI framework  1920  may be the same or different than the AI frameworks  1030 ,  1332  as previously described. The plant detection artificial intelligence framework  1920  may determine the treatment path, at least in part, by an amount of pesticide required for the number and type of weeds  2320 ,  2322  found and/or the amount of herbicide or fungicide present in the reservoir. 
     The determination of the treatment path may be determined at least in part based on the battery level and spray available for a particular drone  202 ,  1200  to ensure that the drone  202 ,  1200  has enough power to return to the base station  204 . When the mission exceeds either the battery capacity or spray capacity (or both), the drone  202 ,  1200  may execute as much of the mission as possible while ensuring the drone  202 ,  1200  has enough battery capacity to return to the base station  204 . Once the drone  202 ,  1200  reaches the battery capacity necessary to return to the base station  204 , the drone  202 ,  1200  stops treatment, records a return position, and returns to the base station  204 . The drone  202 ,  1200  then swaps the batteries  618  and/or spray canister  302 . The drone  202 ,  1200  returns to the return position and resumes the mission. The drone  202 ,  1200  may continue to repeat this process until the mission is complete. In some aspects, the treatment path may be transmitted to one or more other drones  202 ,  1200  in order to perform treatment on the field  2300 . 
     In one aspect, a high-altitude survey may be performed using the camera  630  to achieve a sub-millimeter resolution, which may be fed to an online or offline AI framework  1030  to determine the pest-type and location and plan a general flight plan and/or pest location(s) for one or more drones  202 . The mission planning  1004  may break the field  2300  into drone-sized squares (e.g. approximately equal to a wingspan of the drone  202  being used), and plan a flight using a Dijkstra pattern to optimally treat only the drone-sized squares containing pests. The treatment drone  1200  follows the mission. However, because of environmental factors such as wind and limiting factors such as GPS position at that small a detail, an AI framework  1920  may be present in the treatment drone  1200  where the AI framework  1920  may further refine the position for the pest treatment within the 4-inch×4-inch treatment area. In another aspect, the AI framework  1920  may be used to create one or more missions and/or one or more treatment plans and configured to output a data format for a high clearance sprayers and/or other non-drone machinery. 
     Collisions may be avoided by using radar, lidar, and/or binocular imaging and computer vision as previously described. In another aspect, a height of the terrain may be determined also using radar, lidar, and/or binocular imaging and computer vision. Turning to  FIG. 24 , a no altitude adjustment configuration  2410  demonstrates the aerial drone  202  having a fixed flight height (e.g.  10 - m ) above sea level. In this configuration  2410 , the drone  202  may crash  2412  into terrain  2414  having a flight height. In a terrain avoiding configuration  2420 , the aerial drone  202  may increase the flight height when the terrain  2422  is higher than the current flight height but not reduce the flight height following the increase (such as the valley  2424 ). In this aspect, the aerial drone  202  assumes that surrounding terrain may have a similar maximum terrain height. A terrain following configuration  2430  may have the drone  202  follow one or more contours of the terrain to maintain a consistent elevation above the terrain rather than above sea level. The terrain following configuration  2430  may keep a consistent distance from the ground and may adjust a camera angle proportionate to the inclination/declination, such that many images appear consistent so as to minimize variance between individual images. This may be implemented with binocular cameras  806 , a single camera  806  and spatial analysis techniques and/or lidar to determine a change in topography. In some aspects, similar techniques may be used to determine crop height and adjust the flight height so that the drone  202  may stay above a maximum crop height. 
     In some instances, the aerial drone  902  may fly outside of a boundary of the field  2300  while turning  2310 . If the drone  902  deviates significantly from the field  2300 , the drone  902  may compute a trajectory necessary to return the drone  902  to the mission path. If the drone  902  is unable to return to the mission path, then the drone  902  may return to the base station  204 . 
     According to some aspects, the pest detection AI framework  1030  may be able to determine a maturity of the weed  2320 ,  2322 . The pest detection AI framework  1030  may then prioritize weeds  2320 ,  2322  that are approaching seed maturity in order to eliminate 99% of weeds within the field  2300  prior to seed maturity. The AI framework  1030  may track identified weeds  2320 ,  2322  in order to track a growth progress of the weed  2320 ,  2322  in order to determine an optimal treatment time to reduce herbicide use. The tracking of identified weeds  2320 ,  2322  may be based at least on phenotype. For example, some small weeds may optimally be destroyed early in order to minimize seeding, while other weeds may be permitted to grow to a size where the weed may absorb more of the herbicide. 
     Turning to  FIGS. 26A to 26E , the base station  204  may comprise a plurality of compartments  2602 . The compartments  2602  comprise a generally square shape when viewed from above having three closed sides  2604  and one side configured to open. The sides  2604  may be coupled to a top  2610  and a bottom  2612  of the compartment  2602  via one or more angular support members  2606 . The three closed sides  2604  may comprise one or more ventilation holes  2620  to facilitate air flow through the compartment  2602 . In some aspects, these storage trays may comprise a battery swap system, a battery recharge system, and/or the refilling system. In some aspects, the onboard data may be transferred via a cable, Wi-Fi, and/or Bluetooth while the drone  202  is within the compartments  2602 . 
     The side configured to open may have a hinged door  2608  that may open when the drone  202  becomes activated within the compartment  2602  and/or approaches the compartment  2602  if the drone  202  is currently on a mission. The hinged door  2608  may be spring loaded to remain shut until a sliding tray  2614  pushes the door  2608  open. The sliding tray  2614  receives the landing drone  202 . Each sliding tray  2614  may comprise one or more tracks  2618  or belts  2618  for horizontally sliding the tray  2614  with respect to the compartment  2602 . Each compartment  2602  has a height slightly larger than the height of the drone  202  stored therein. The top  2610  may comprise a frame  2616  to facilitate attachment to the bottom  2612  of the compartment  2602  above. In the aspect presented in  FIG. 26A , the base station  204  comprises four compartments  2602  with each of the compartments  2602  having an opening pointing in a different direction. 
     In other aspects, only the bottom  2612  of the compartment  2602  may be present and the bottom  2612  of the compartment  2602  above may serve as the top  2610  of the compartment below  2602 . In such a configuration, a top  2610  may be affixed to the uppermost compartment  2602 . In other aspects, the uppermost compartment  2602  may be left open and one or more weather monitoring and/or communication modules may be placed therein. 
     Although the aspects described herein demonstrate the detection of pests and ignore non-pests (e.g. such as crop, bushes, physical objects like a cans, rocks, etc. lying on the field surface), other aspects may detect the crop and treat all non-crop areas as undesirable. In this aspect, some or all non-crop areas may be treated. In the first aspect, the detection of pests may be useful for treatment after seeding where only the pests are treated. In the other aspect, the non-crop areas may be treated in a burn-down phase with a fast-moving vehicle that sprays anything between crop rows indiscriminately, which may be more energy and/or time efficient with a less computational power requirement. 
     In some aspects, the treatment system  200  may be configured to travel along a road or railroad in order to treat growth along the road or railroad. 
     In another aspect, pests and crop plants may be determined by way of a chemical signature in addition to spectral signature and/or geometer. For example, the chemical signature may be a flowering plant emitting a particular pollen, which may be detected optically based on environment, such as a yellowing of the nearby dirt, and/or using a separate chemical sensor. In another example, an acoustic signature may comprise using a resonant frequency of the plant and/or the pest to stimulate a detectable phenomenon, such as using sound waves of a specific frequency to repel or attract insects/pests to where the pest may be observed by the camera(s)  630  as described herein. 
     According to the aspects herein, the aerial drone  100  may perform spraying of the weeds  2320 ,  2322 . In other aspects, the aerial drone  902  may instruct a ground-based drone  904  to navigate to the weed positions for eradication. 
     Although the aspects described herein demonstrate the refilling system  300  for the canisters  302 , other aspects may have canisters  302  that may be self-contained canisters that are merely swapped at the base station  204 . 
     Although the aspects herein describe features particular to the aerial drone  202 , other aspects may equally apply to the rolling drone  1200  and vice-versa consistent with the understanding of one of skill in the art on reviewing the description herein. 
     Although the aspects described herein demonstrate the drones  202 ,  1200  returning to a stationary base station  204 , other aspects may have the drones  202 ,  1200  returning to a mobile base station  204 . In some aspects, the mobile base station  204  may be the rolling drone  1200  and the aerial drones  202  may return to the rolling drone  1200 . 
     Although the aspects described herein demonstrate the aerial drone  202  having a camera  806  and a spraying system, other aspects may have smaller aerial drones  202  with only a camera  806  in order to reduce the amount of propeller wash. 
     As described in further detail herein, various aspects of the treatment systems  200  may include capabilities for automatic target detection, selection, prioritization, deselection, re-selection, active stabilization, automatic spray vector solutions, target tagging, and/or continuous or intermittent target tracking. 
     Although the steps of detecting multiple objects, identifying and verifying and prioritizing multiple targets, calculating multiple spray vectors, determining the success of multiple sprays and using the spray success determinations as an input to subsequent spray vector calculations may be shown as single systems or subsystems. One skilled in the art will understand that these systems, subsystems or portions thereof, can be combined, used in a different sequence than shown here, simplified and in some cases omitted to achieve a less complex and less resource intensive design. 
     Various components, subcomponents and parts can be used to achieve, implement and practice the processes, computations, techniques, steps, means and purposes described herein. The embodiments and inventions contained herein may be practiced in various forms and approaches as selected by one skilled in the art. For example, these processes, computations, techniques, steps, means and purposes described herein may be achieved and implemented in hardware, software, firmware or a combination thereof. The drone systems described herein may be contained in a single drone or may be distributed within and across multiple drones and base stations in communication with one another and in any combination. The computing components and processes described herein can be distributed across a fixed or mobile network or both at the same time or different times. For example, some processing may be performed in one location using a first processor while other processing may be performed by another processor remote from the first processor. Other components of computer system may be similarly distributed. As such, computer system may be interpreted as a distributed computing system that performs processing in multiple locations. In some instances, computer system may be interpreted as a single computing device. A drone system described herein may switch back and forth from execution as a self-contained data and computational drone system or part of a distributed computational and data drone system. 
     One skilled in the art may choose hardware implementations for the processing units using one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described above, and/or a combination thereof. 
     One skilled in the art may choose implementations including hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and/or any combination thereof. Software, firmware, middleware, scripting language, and/or microcode implementations may have the program code or code segments to perform the necessary tasks stored in a machine-readable medium such as a storage medium. A code segment or machine-executable instruction may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures, and/or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, and/or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc. 
     One skilled in the art may choose implementations including firmware and/or software utilizing modules (e.g., procedures, functions, algorithms, etc.) that perform the processes described herein. Any machine-readable medium tangibly embodying instructions may be used in implementing the processes and methodologies and techniques described herein. For example, software codes may be stored in a memory. Memory may be implemented within a processor or external to a processor. “Memory” as used herein refers to any type of long term, short term, volatile, nonvolatile, or other storage medium and is not to be limited to any particular type of memory or number of memories, or type of media upon which memory is stored. 
     Moreover, as disclosed herein, the term “storage medium” may represent one or more memories for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The term “machine-readable medium” includes, but is not limited to portable or fixed storage devices, optical storage devices, and/or various other storage mediums capable of storing that contain or carry instruction(s) and/or data. 
     The computer systems described herein may use without limitation one or more general-purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, video decoders, and/or the like). The computer systems described herein may use and or configure storage devices to implement any appropriate data stores, including without limitation, various file systems, database structures, database control or manipulation or optimization methodologies. 
     The methods, systems, and devices discussed above are examples. Various configurations may omit, substitute, or add various procedures or components as appropriate. For instance, in alternative configurations, the methods may be performed in an order different from that described, and/or various stages may be added, omitted, and/or combined. Also, features described with respect to certain configurations may be combined in various other configurations. Different aspects and elements of the configurations may be combined in a similar manner. Also, technology evolves and, thus, many of the elements are examples and do not limit the scope of the disclosure or claims. The foregoing is considered as illustrative only of the principles of the invention. Further, since numerous changes and modifications will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation shown and described, and accordingly, all such suitable changes or modifications in structure or operation which may be resorted to are intended to fall within the scope of the claimed invention.