Patent Description:
Effective and efficient agricultural operation is highly dependent on reliable knowledge of soil nutrients and other soil characteristics. Such nutrients and other characteristics may vary not only from region to region, but also between smaller areas within a single field. It is beneficial for an operator to understand the various soil characteristics across the field.

<CIT> describes a system for soil analysis in which a vehicle is moved over a field and soil samples are taken from the ground. These samples are mixed with a liquid to form a colorless solution, which is then divided into a number of sub-samples to which specific reagents are mixed. Each of these sub-samples is analyzed by a photometric sensor determining the optical transmission at a specific wavelength. Based on a unique calibration curve which correlates a photometric reading with nutrient concentration, the nutrient contents of the soil are determined and used for controlling a fertilizing system.

<CIT> and <CIT> describe methods for determining a fertilizing map based on soil data, crop growth data and user input.

<CIT> describes calibration of sensor data sensed on different machines.

A soil analysis system is provided for an agricultural vehicle and includes a sensor apparatus, a controller coupled to the location sensor and the infrared sensor, and a display device coupled to the controller. The sensor apparatus includes a location sensor configured to determine a location of the agricultural vehicle; and an infrared sensor configured to collect infrared spectra from soil at the location. The controller is configured to determine a soil type based on the location; select at least one nutrient calibration curve based on the soil type at the location; analyze the infrared spectra according to the at least one nutrient calibration curve to generate at least one estimated nutrient value for the soil at the location; and generate display commands representing the at least one estimated nutrient value. The display device is configured to generate a first display representing the at least one estimated nutrient value based on the display commands.

The processor can also generate a fertilizer recommendation based on the estimated nutrient value for the location; and generate actuator signals to selectively apply a fertilizer onto the field at the location based on the fertilizer recommendation.

Other features and advantages will become apparent from the description, the drawings, and the claims.

The following describes one or more example embodiments of the disclosed system and method, as shown in the accompanying figures of the drawings described briefly above. Various modifications to the example embodiments may be contemplated by one of skill in the art.

An operator may use a work vehicle in the form of an agricultural planting vehicle to plant seeds, seedlings, plants, root stock, bulbs, or other crop precursors in rows over a field during a planting event. Clearly, it is beneficial for an operator to know and understand the soil characteristics of the field, including soil type and constituent nutrients. Such characteristics may vary based on geographic region, and moreover, may vary greatly within the field. Determining such soil information may be challenging. Laboratory testing may be expensive, particularly without a clear understanding of the specific field areas at which to test. Some commercial characteristic mapping services may be generally available, but typically, only with respect to broad geographical regions.

The present disclosure generally relates to a system and method for analyzing soil within a field of an operator, more specifically for identifying, evaluating, and classifying of soil characteristics, particularly soil nutrients, and recommending responses to such soil characteristics. Generally, the system and method may be primarily implemented within a field, representing a relatively small area (e.g., as opposed to a state or national region) in an agricultural operation. However, portions may be implemented with a soil analysis center and/or based on knowledge from external sources and/or with information from other operators. The system and method may be at least partially implemented during an agricultural operation with soil engagement, e.g. planting, tilling, or closing operations, in which soil is analyzed in approximately real time or in which soil data is collected, stored, and subsequently analyzed.

As described in greater detail below, the soil analysis system and method may collect and consider sensor information associated with soil at a location. The information collected by the sensor apparatus may include near-infrared spectra reflected by the soil and/or mid-infrared spectra reflected by the soil, as well as other soil parameters such as temperature, pH, moisture, and the like. Collectively, the soil information at the particular location may be considered a sensor-based soil data set. The sensor-based soil data sets may be evaluated with respect to soil type (and other parameters) by identifying one or more wavelength peaks associated with particular nutrients or other soil characteristics and application of one or more calibration curves to quantify or qualify the nature of the nutrients or other soil characteristics. In turn, and upon consideration of multiple sensor-based data sets, the soil analysis system and method may generate nutrient zone maps with nutrient zones and may further generate recommendations in response to the nutrient zone maps. Further, the soil analysis system and method may recommend soil locations for collection of soil samples for laboratory analysis to generate laboratory-based soil data sets with information such as collection location, near-infrared spectra reflected by the soil, mid-infrared spectra reflected by the soil, additional soil parameters, and in particular, nutrient and other characteristic values as determined by the laboratory (e.g., with chemical and/or a more thorough mass spectrometry analysis). The soil analysis system and method may use the laboratory-based soil data sets and resulting nutrient and characteristic values to modify the calibration curves used for analyzing the sensor-based data sets.

In some examples, the soil analysis system and method may use soil type as a criterium for selection of the calibration curve for analyzing a sensor-based soil data set. Generally, soil type is defined by the dominating size of the particles within the soil, such as sand, clay, silt, peat, chalk and loam types of soil. Soil type may have various types of classification nomenclature. For example, the US Department of Agriculture (USDA) defines soil taxonomy with six hierarchical levels (suborder, great group, subgroup, family, and series) that define soils based on relative sand, silt, and clay percentages, as well as other characteristics. Generally, the soil type from the USDA is based on historical sampling.

The following describes one or more example implementations of the disclosed soil analysis systems and methods for a field, as shown in the accompanying figures of the drawings described briefly above. Generally, the disclosed systems and methods operate in the context of an agricultural work machine in order to monitor, evaluate, and display soil information that provide for improved efficiency, operation, and production as compared to conventional systems and techniques.

<FIG> is an example environment in which an in-field soil analysis system <NUM> may be implemented in order to monitor, display, evaluate, and/or advise on crop soil nutrients and other characteristics. Generally, the in-field soil analysis system <NUM> may be implemented in, or associated with, one or more work vehicles <NUM> (one of which is shown) and/or data and/or processing sources <NUM>, <NUM>, including a soil analysis center <NUM>, and a soil type mapping database <NUM>, that may communicate over a network <NUM>. Although one work vehicle <NUM> is depicted in <FIG> and discussed in greater detail below as an example, the in-field soil analysis system <NUM> may be used with respect to any number of vehicles performing the same or different work functions as further data sources to improve the soil analysis discussed below. In various examples, the in-field soil analysis system <NUM> may be part of a distributed system (e.g., with the soil analysis center <NUM>, the soil type mapping database <NUM>, personal computing devices, and/or other vehicles cooperating with vehicle <NUM>) and/or a stand-alone system. In some examples, the soil analysis center <NUM> and soil type mapping database <NUM> may be omitted. An introduction of the work vehicle <NUM>, the soil analysis center <NUM>, and the soil type mapping database <NUM>, will be provided below prior to a more detailed description of operation of the in-field soil analysis system <NUM>.

In one embodiment, the work vehicle <NUM> is in the form of a tractor <NUM> that tows a planting apparatus <NUM> (e.g., such that the work vehicle <NUM> may be considered an agricultural planting vehicle or planter). The tractor <NUM> has a vehicle frame <NUM> supporting the cab <NUM>, and generally, the tractor <NUM> includes a powertrain <NUM> supported on the frame <NUM> that generates power for propulsion and/or other tasks to be performed by the work vehicle <NUM>. In one example, the powertrain <NUM> may include an engine, transmission, steering system, wheels, and the like for propelling and maneuvering the work vehicle <NUM>, either autonomously or based on commands by the operator. The work vehicle <NUM> may include various other components or systems that are typical on work vehicles. Examples include actuation systems, lubrication and cooling systems, battery systems, exhaust treatment systems, braking systems, and the like.

In this example, the planting apparatus <NUM> is towed behind the tractor <NUM> to dispense seeds, root stocks, or crop precursors as the work vehicle <NUM> traverses the field, either automatically or based on commands from the operator. As such, the planting apparatus <NUM> may include any suitable components, including supply bins, actuators, controllers, frames, valves, wheels, openers, tanks, meters, shanks, and the like. It should be noted that the soil analysis system <NUM> may be used with respect to any suitable vehicle, planting apparatus, agricultural machine or vehicle, or other type of work vehicle or machine. In particular, and as discussed in greater detail below, the planting apparatus <NUM> may include one or more openers or shanks that engage with the soil as the vehicle <NUM> traverses the field, thereby enabling the soil analysis system <NUM> to interact with the soil for collection of soil data, as described below.

The work vehicle <NUM> may further include a vehicle controller <NUM> (or multiple controllers) to control various aspects of the operation of the work vehicle <NUM>. For example, the vehicle controller <NUM> may also facilitate automatic or manual maneuvering of the vehicle traversing the field and actuation of the planting apparatus <NUM> during a planting event. Additionally, in some embodiments, the vehicle controller <NUM> may implement any or all (or none) of the functions of the soil analysis system <NUM> discussed herein.

Generally, the vehicle controller <NUM> (or others) may be configured as a computing device with associated processor devices and memory architectures, as a hard-wired computing circuit (or circuits), as a programmable circuit, as a hydraulic, electrical or electro-hydraulic controller, or otherwise. As such, the vehicle controller <NUM> may be configured to execute various computational and control functionality with respect to the work vehicle <NUM>, the tractor <NUM>, the planting apparatus <NUM>, and/or the soil analysis system <NUM>. In some embodiments, the vehicle controller <NUM> may be configured to receive input signals in various formats from a number of sources (e.g., including from the operator via operator input devices <NUM>, one or more sensor apparatuses <NUM>, units, and systems onboard or remote from the work vehicle <NUM>, and/or other aspects of the soil analysis system <NUM>); and in response, the vehicle controller <NUM> generates one or more types of commands for implementation by the various systems on or outside the work vehicle <NUM>.

As one example discussed in greater detail below, the vehicle controller <NUM> may facilitate operation of the soil analysis system <NUM>, particularly with respect to collecting, evaluating, displaying, sending, and making recommendations associated with soil information. Initially, the collected soil data may be in the form of raw data from the sensor apparatus <NUM> described below (or other sources) or undergo some processing in the vehicle controller <NUM> in order to extract the desired characteristics or parameters. Moreover, in some examples, the vehicle controller <NUM> may also implement one or more aspects of the soil analysis system <NUM> described below with respect to the soil analysis center <NUM> and/or the soil type mapping database <NUM>. Further details will be provided below.

In some embodiments, the vehicle controller <NUM> may be configured to receive input commands and to interface with an operator via human-vehicle interfaces in the forms of one or more operator input devices <NUM> and/or one or more display devices <NUM>, which may be disposed inside the cab <NUM> of the work vehicle <NUM> for easy access by the vehicle operator. The operator input devices <NUM> may be configured in a variety of ways. In some embodiments, the one or more operator input devices <NUM> may include devices with one or more joysticks, various switches or levers, one or more buttons, a touchscreen interface, a keyboard, a speaker, a microphone associated with a speech recognition system, or various other human-machine interface devices. As described in greater detail below, the operator may use the operator input devices <NUM> to steer the work vehicle <NUM> during an agricultural event, to interact with the planting apparatus <NUM>, and/or to interact with the soil analysis system <NUM> and the display device <NUM> to view soil analysis information. The display device <NUM> may be implemented as a flat panel display or other display type that is integrated with an instrument panel or console of the work vehicle <NUM>. The display device <NUM> may include any suitable technology for displaying information, including, but not limited to, a liquid crystal display (LCD), light emitting diode (LED), organic light emitting diode (OLED), plasma, or a cathode ray tube (CRT). As described in greater detail below, the display device <NUM> may function to render one or more types of soil information generated in accordance with operation of the soil analysis system <NUM>.

The work vehicle <NUM> further includes a vehicle communication component <NUM>. The vehicle communication component <NUM> enables communication between the vehicle controller <NUM>, the soil analysis center <NUM>, soil type mapping database <NUM>, and other information sources. The vehicle communication component <NUM> includes any suitable system for sending and/or receiving data, including directly (e.g., via Bluetooth®, radio frequency signals, or the like) or via network <NUM>. Thus, the vehicle communication component <NUM> may include a network interface or adapter, a Bluetooth® transceiver, a radio transceiver, a cellular transceiver, an LTE transceiver and/or a Wi-Fi transceiver. The network <NUM> may include or otherwise cooperate with the JDLink™ system commercially available from Deere & Company of Moline, Illinois.

The work vehicle <NUM> further includes one or more sensor apparatuses <NUM> on the tractor <NUM> and/or planting apparatus <NUM> that function to collect information associated with the work vehicle <NUM> and the associated environment. Such information may be provided to the vehicle controller <NUM> and/or the vehicle communication component <NUM> for potential transmission and use by the soil analysis system <NUM>. In one example, discussed below, the sensor apparatus <NUM> includes a location or position sensor, a light source, a near-infrared sensor, a mid-infrared sensor, and one or more auxiliary sensors that collectively function to facilitate generation of sensor-based soil data sets, discussed below. Other sensors and associated components may be provided.

Additionally, the work vehicle <NUM> may include a sampling mechanism <NUM> for selectively collecting a portion of soil material as the vehicle <NUM> traverses the field. The sampling mechanism <NUM> may include a scoop or a coring device that is actuated to engage the soil, collect the sample, and store the sample. As described below, the collected sample may be laboratory tested (e.g., chemically tested in some manner on the vehicle, by the operator, or by a third party). In one example, the sampling mechanism <NUM> may be onboard the work vehicle <NUM> with the other aspects of the soil analysis system <NUM> discussed herein, while in other examples, the sampling mechanism <NUM> may be located on another vehicle or machine, or the sampling mechanism <NUM> may be omitted and collection may be manually performed by the operator or other party.

In some examples, the work vehicle <NUM> may further include a fertilizer applicator <NUM> that operates to distribute fertilizer (generally, any type of nutrient) onto or into the field. The fertilizer applicator <NUM> may operate based on actuator commands or signals from the controller <NUM> generated automatically in response to soil analysis and/or based on commands from the operator via the input devices <NUM>. Any suitable type of fertilizer applicator <NUM> may be provided for distributing any type or form of fertilizer, including spinner spreaders, air booms, side-dress rigs, top-dress applicators, and the like for distributing solid, liquid, or gaseous forms of fertilizer. As discussed in greater detail below, the soil analysis system <NUM> may operate to selectively direct an appropriate amount of fertilizer to each individual location at which sensor data is collected.

It should be noted that various aspects of the work vehicle <NUM> that interact with the soil analysis system <NUM> and other vehicle systems may be embodied as a personal device associated with the vehicle operator and/or the work vehicle <NUM>. Such aspects may include one or more functional units of the vehicle controller <NUM>, operator input device <NUM>, display device <NUM>, vehicle communication component <NUM>, and sensor apparatus <NUM>. Such devices implementing the soil analysis system <NUM> associated with the work vehicle <NUM> may include servers, desktop computers, laptop computers, tablet computers, or other mobile devices, such as palm top computers, cell phones, smart phones, multimedia players, personal digital assistants, etc..

As noted above, the soil analysis center <NUM> may be in communication with the work vehicle <NUM> to implement one or more functions of the soil analysis system <NUM>, such as to receive and evaluate soil information from one or more operators to facilitate one or more aspects of the soil analysis system <NUM>, discussed below. In some examples, the soil analysis center <NUM> may operate as "backend" system or server that facilities operation within a field or fields, including the collection and creation of various types of data.

Generally, the soil analysis center <NUM> may be considered to have at least one soil analysis center controller <NUM> and at least one communication component <NUM>, as well as data stores, interface components, and the like (not shown in <FIG>). In one example, the soil analysis center controller <NUM> and soil analysis center communication components <NUM> may have similar elements, characteristics, and functionality to the vehicle controller <NUM> and vehicle communication component <NUM>, respectively, as discussed above. As such, the soil analysis center controller <NUM> is in communication with the work vehicle <NUM> via the soil analysis center communication component <NUM> over a suitable interconnection architecture or arrangement that facilitates transfer of data, commands, power, etc., such as network <NUM>, to implement one or more aspects of the soil analysis system <NUM>, including providing requested or desired data for carrying out the associated functions. In some examples, the soil analysis center <NUM> may be omitted.

Briefly, in one example, the system <NUM> may include or otherwise interact with a soil type mapping database <NUM> as a data source in which soil types are stored according to location. In one example, the soil type mapping database <NUM> maintained by the USDA. Other types of external databases and systems may also be accessed by the soil analysis system <NUM>.

The view of <FIG> provides an example schematic operation of the soil analysis system <NUM> with dataflows into, out of, and within the vehicle controller <NUM>, as well as the display device <NUM>, the sensor apparatus <NUM>, the sampling mechanism <NUM>, and applicator <NUM>. As also shown in <FIG>, the soil analysis system <NUM> may include or interact with a soil type mapping database <NUM>, as introduced above and as discussed in further detail below.

Generally, the soil analysis system <NUM> may be considered to include the controller <NUM>, the sensor apparatus <NUM>, and display device <NUM>. The soil analysis system <NUM> may optionally include a sampling mechanism <NUM>, applicator <NUM>, and one or more additional controllers, such as center controller <NUM>, and data sources, such as a soil type mapping database <NUM>.

The view of <FIG> additionally provides further details about one example of the sensor apparatus <NUM>. As shown, the sensor apparatus <NUM> includes a positioning system device (or other type of location device) <NUM>, a light source <NUM>, a near-infrared sensor <NUM>, a mid-infrared sensor <NUM>, and one or more auxiliary sensors <NUM>. As introduced above, one or more portions of the sensor apparatus <NUM> may be positioned proximate to the soil such that data associated with the soil may be collected. One or more collected parameters at a location may form a sensor-based data set.

In one example, the location device <NUM> may be any type of satellite positioning system device (e.g., Global Positioning System (GPS) in the United States or other satellite positioning systems used in other parts of the world). Generally, the location device <NUM> operates to ascertain a location for the work vehicle <NUM> within the field, particularly when collecting information making up the soil data sets.

The light source <NUM> may be any suitable type of light source that functions to illuminate the area proximate to the engaged soil to enable operation of the near-infrared sensor <NUM> and mid-infrared sensor <NUM>. The near-infrared sensor <NUM> operates to detect the reflectance of light from the soil within the near-infrared spectrum; and the mid-infrared sensor <NUM> operates to detect the reflectance of light from the soil within the mid-infrared spectrum. In one example, the near-infrared spectrum may be considered wavelengths between <NUM> and <NUM>, and the mid-infrared spectrum may be considered wavelengths between <NUM> and <NUM>, although the ranges for analysis may vary. Various prisms and fiber optic components may be provided as part of the sensor apparatus <NUM> to facilitate collection of the data.

The auxiliary sensors <NUM> may include additional soil or environmental sensors, including one or more moisture sensors, one or more pH sensors, and/or one or more temperature sensors. As discussed below, the sensor apparatus <NUM> functions to collect information associated with the soil, and particularly generates infrared spectra and other parameters (e.g., moisture, pH, temperature, etc.) at a collection location as a sensor-based soil data set. Additional details are provided below.

In this example, the vehicle controller <NUM> may be considered to be organized as one or more functional units or modules <NUM>, <NUM>, <NUM>, and <NUM> (e.g., software, hardware, or combinations thereof), as well as one or more types of data storage <NUM>. As an example, each of the modules <NUM>, <NUM>, <NUM>, <NUM> and data storage <NUM> may be implemented with processing architecture such as a processor <NUM> and memory <NUM>. For example, the controller <NUM> may implement the modules <NUM>, <NUM>, <NUM>, <NUM> and data storage <NUM> with the processor <NUM> based on programs or instructions stored in memory <NUM>. In the depicted embodiment, the modules <NUM>, <NUM>, <NUM>, <NUM> include an analysis module <NUM>, a nutrient mapping module <NUM>, a recommendation module <NUM>, and a feedback module <NUM>. Generally, the data storage <NUM> may function to store location-based calibration curves, as well as to store and/or enable accessibility of one or more of the other types of data discussed below.

The data flows and organization depicted in <FIG> are merely examples, and other mechanisms for performing similar functions may be provided, certain functions may be omitted, and additional functions may be added. Moreover, although depicted in the vehicle controller <NUM>, one or more of the modules <NUM>, <NUM>, <NUM>, <NUM>, the data storage <NUM>, and other operational functionality described below may be implemented within the vehicle controller <NUM> or within a separate device (e.g., such as a personal computer, smartphone, or the like). In some examples, the soil analysis center controller <NUM> may also receive similar data and perform similar operations with respect to the other work vehicles associated with the field of the operator or in other fields. Additional details about operation of these modules <NUM>, <NUM>, <NUM>, <NUM> and data storage <NUM> will be provided below with reference to <FIG> and <FIG>.

From the context of the work vehicle <NUM>, implementation of the soil analysis system <NUM> may be enabled or activated in a number of ways. In one example, the first work vehicle <NUM> may collect soil data and other types of data based on a manual activation by the operator (e.g., via the operator input device <NUM>). In further examples, the work vehicle <NUM> may implement the soil analysis system <NUM> based on an automatic activation, e.g., upon crossing a geographical boundary, upon actuation the planting apparatus <NUM>, or upon actuation of another type of soil engaging activity.

Reference is briefly made to <FIG>, which is schematic view of an example field <NUM> in which the soil analysis system <NUM> is implemented. In this example, the field <NUM> is depicted with defined outer boundaries <NUM> within which a number of sensor-based soil data sets have been collected at locations 204a-204r for consideration by the system <NUM>, described below. The locations 204a-204r associated with the sensor-based soil data sets may be selected for any suitable reason, including time or distance frequencies, geographical spacings, and/or based on recommendations.

As introduced above, during operation, the sensor apparatus <NUM> generates sensor-based soil data sets, each set with the location, infrared spectra, and other parameters, that are received by the controller <NUM>. In other words, the locations 204a-204r depicted in <FIG> represent sensor-based soil data sets collected over time. In the discussion below, the analysis of the sensor-based data sets may be performed in real time or collectively.

Although not shown, the controller <NUM> may include an input module or unit configured to condition and/or distribute the sensor-based data sets and other data received by controller <NUM>. For example, the data may be filtered, identified, sorted, and the like prior to being distributed to the modules <NUM>, <NUM>, <NUM>, <NUM> and/or data storage <NUM>.

In one example, the analysis module <NUM> receives and considers the sensor-based soil data set. Based on the location associated with soil data set, the analysis module <NUM> may determine a soil type associated with the location at which the soil data set was collected. In one example and as shown in <FIG>, the analysis module <NUM> may send the location to the soil type mapping database <NUM>, and in return, receive a soil type for the location or a soil type map for the field from which the soil type for the location may be determined. In a further example, the soil type map for the field may be retrieved from data storage <NUM> such that the analysis module <NUM> may determine the soil type for the location without using an external database such as the soil type mapping database <NUM>.

The view of <FIG> depicts a soil type map <NUM> for the field <NUM> on which similar soil types for sensor-based data sets are determined, clustered, and extrapolated as soil type zones 302a-302e overlaid one an image of the field <NUM>. In one example, the analysis module <NUM> may, in effect, build such a map <NUM> for the field <NUM> by individually determining soil types of sampling locations (e.g., by sending location coordinates and receiving a soil type in response) over time such that the map <NUM> may be updated as additional soil data is collected and analyzed. In other examples, the soil type map <NUM> may be available from an external source, such as database <NUM>.

In the example depicted in <FIG>, the soil type zone 302a corresponds to clime silty clay on <NUM> to <NUM>% slopes; soil type zone 302b corresponds to clime silty clay on <NUM> to <NUM>% slopes; soil type zone 302c corresponds to Clime-Hobbs complex on <NUM> to <NUM>% slopes; soil type zone 302d corresponds to Irwin silty clay loam at <NUM> to <NUM>% slopes; and soil type zone 302e corresponds to Rosehill silty clay at <NUM> to <NUM>% slopes. These soil types are merely examples. As shown, the soil type varies across the field <NUM>. In any event, the soil type map <NUM> may be displayed to the operator via the display device <NUM> and/or stored for subsequent access in order to determine soil type in response to subsequent collection of sensor-based data sets. In some examples, the soil type map <NUM> may be provided in relative real time on the vehicle <NUM>, including when the vehicle <NUM> is operating within the field.

As noted above, the analysis module <NUM> may also receive an infrared spectra representation in the near-infrared range and the mid-infrared range as part of the soil data set. Generally, the analysis module <NUM> processes the infrared spectra representations with infrared spectroscopy techniques in order to generate estimated nutrient values for the soil at the respective location.

Generally, infrared spectroscopy exploits the fact that molecules (e.g., molecules associated with nutrients or other soil characteristics) absorb frequencies that are characteristic of respective molecular structure. Such frequencies and associated wavelengths for particular nutrients and soil characteristics may be identified by comparing the spectra to reference spectra. In one example, one or more wavelength peaks may represent a nutrient or other soil characteristic. The value of such a nutrient or soil characteristic may be determined by application of a calibration (or standard) curve. The selection of the wavelength peaks and the subsequent application of the calibration curves may be dependent on a number of parameters, including soil type and other soil characteristics (e.g., moisture, pH, and temperature). Typically, a calibration curve operates as a mechanism for determining the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration, e.g., based on empirical observations that may be extrapolated to samples of unknown concentrations. Each calibration curve may be linear or non-linear and generated with regression analysis or other modeling mechanisms, including neural network or machine learning techniques, discussed below.

Briefly, reference is made to <FIG>, which is an example infrared spectra plot <NUM> with spectra representations 402a-402e depicted for a sample collection of sensor-based soil data sets. In <FIG>, the plot <NUM> includes wavelength represented on the horizonal axis and percentage (%) of reflection or transmittance represented on the vertical axis. As shown, spectra representations 402a-402e are depicted at wavelengths of <NUM> - <NUM>, e.g., over at least portions of the near-infrared range and the mid-infrared range. During operation, the analysis module <NUM> operates to evaluate each of the infrared spectra representations 402a-402e, identify one or more of the wavelength peaks that correspond to the nutrients of interest, and apply one or more calibration curves to the one or more wavelength peaks in order to estimate or otherwise determine one or more nutrient values.

Returning to <FIG>, in one example, the analysis module <NUM> may retrieve a calibration curve from the data storage <NUM> based on one or more of the location and/or soil type associated with the location. In particular, the data storage <NUM> may have one or more calibration curves that are calibrated according to the soil type such that the analysis module <NUM> may retrieve the calibration curve associated with the soil type for the location of the sensor-based soil data set. In some example, the calibration curve may be selected and/or modified based on parameters in addition to location and/or soil type, such as moisture, pH, temperature, and the like. In further examples, discussed below, the calibration curves of the data storage <NUM> may be derived or dependent on additional parameters, such as laboratory-based soil data sets. In effect, the calibration curves may be derived and/or otherwise enabled for the particular location and local characteristics of the soil data set, as opposed to a generalized calibration curve for a wider area or region. In particular, and as discussed in greater detail below, the selected calibration curves may initially be based on soil type and relatively generic known nutrient characteristics for that soil type; and in further examples, the calibration curves may be supplemented or modified based on laboratory testing of soil samples from the field of the operator and/or from fields of other operators with similar soil types. Such updated calibration curves may be generated with neural networks or machines learning techniques.

As such, the analysis module <NUM> generates one or more estimated nutrient values for the location associated with the soil data set. The estimated nutrient values may be provided to the display device <NUM> or other interface device for conveying the information to the operator.

Additionally, the estimated nutrient values and corresponding locations may be provided to the nutrient mapping module <NUM>. Over time, the nutrient mapping module <NUM> operates to cluster similar nutrient values in adjacent locations to derive boundaries between areas with relative differences nutrient values as a nutrient zone map. Reference is made to <FIG>, which is an example nutrient zone map <NUM> for the field <NUM> based on the sensor-based soil data sets discussed above. In this example, the nutrient mapping module <NUM> generates boundaries to define "zones" 502a-502d of generally similar nutrient values to form the nutrient zone map <NUM> for the field <NUM>. The nutrient zone map <NUM> defined by the nutrient mapping module <NUM> may provide the operator with additional information about the field and enable each zone to be individually addressed with decisions. In this example, the nutrient zones 502a-502d respective represent different amounts of potassium as the nutrient, e.g. in which the soil of zone 502a has a relatively high amount of potassium as compared to the soil of zone 502b, and so on, to the zone 502d, which has soil with a relatively smaller amount of potassium. Although potassium is represented in map <NUM>, other nutrient (or characteristic) zones may be provided, such as magnesium, phosphorus, moisture, pH, temperature, and the like. The nutrient zone map <NUM> may be provided to the display device <NUM>, as well as data storage <NUM> to be distributed as necessary or desired to other modules <NUM>, <NUM>, <NUM>, <NUM> of the controller <NUM>. In some examples, the nutrient zone map <NUM> may be provided in relative real time on the vehicle <NUM>, including when the vehicle <NUM> is operating within the field.

The recommendation module <NUM> receives nutrient zones from the nutrient mapping module <NUM>. The recommendation module <NUM> evaluates the nutrient zones and, in response, may generate one or more types of recommendations. In particular, the recommendation module <NUM> may consider a nutrient value or values for a zone or zones; identify any deficiency or issue with respect to the nutrient value or values for the zone or zones; and generate a recommendation to remedy or otherwise address the deficiency or issue. As an example, the recommendation module <NUM> may consider the nutrient values for primary nutrients such as nitrogen, potassium, and phosphorus, and if such nutrients are deemed lacking in the respective zone, the recommendation module <NUM> may generate a fertilizer recommendation to supplement or otherwise improve the respective nutrient. The recommendation may be generated by the recommendation module <NUM> in any suitable manner, including with look-up tables, models, machine learning, equations, and the like based on historical, scientific, and/or empirical data.

Reference is made to <FIG>, which is a nutrient recommendation map <NUM> of the field <NUM> in which a number of nutrient recommendation zones 602a-<NUM> have been defined, for example, based on the nutrient zone map <NUM> of <FIG> with respect to potassium as the nutrient. As shown, the nutrient recommendation zones 602a-<NUM> represent clustered areas, each of which have relatively similar additional amounts of nutrient that are recommended for the field. In this example, nutrient recommendation zone 602a requires a relative more additional amount of nutrient as compared to nutrient recommendation zone 602b, and so on, to nutrient recommendation zone <NUM>, which requires relatively little additional amount of nutrient. The recommendation module <NUM> may provide the nutrient recommendation map <NUM> to the display device <NUM> for consideration by the operator and/or stored in data storage <NUM>.

Returning to <FIG>, the nutrient recommendations, either individually or collectively, may be provided to the fertilizer applicator <NUM> in the form of actuator signals or commands. In particular, the controller <NUM> may command the fertilizer applicator <NUM> to dispense a specific amount of fertilizer at the respective location associated with the sensor-based soil data set in accordance with a value based on the generated nutrient recommendation. In other words, the soil analysis system <NUM> may dispense individual amounts of fertilizer based on the nutrient values and recommendations of the analyzed soil, either at the time and place of the sensor collection and soil analysis or subsequently. Such analysis and/or fertilizer application may occur prior to planting, during planting, or subsequent to planting. In some examples, the collection and analysis of sensor-based soil data sets, the generation of nutrient recommendations, and the appropriate application of fertilizer based on the nutrient recommendations may be automated or autonomous.

In one example, the recommendation module <NUM> may also consider the nutrient values within the nutrient zones within the context of the overall field or within an entire zone. In particular, the recommendation module <NUM> may identify areas or locations in which one or more soil data sets are absent, questionable, or potentially out of date. For example, the density of collected data sets for a zone or area may indicate the nature of variations in the nutrient values (e.g., greater variation requires more soil data sets for each area). In one example, the recommendation module <NUM> generates sampling recommendations with locations at which to take further samples or soil data sets. In addition to sampling recommendations and/or fertilizer recommendations, the recommendation module <NUM> may further generate seeding recommendations, soil amendment recommendations, watering recommendations, and/or other agricultural action recommendations. In some examples, the recommendation map <NUM> may be provided in relative real time on the vehicle <NUM>, including when the vehicle <NUM> is operating within the field.

In one example, the sampling recommendations may include sensor collection recommendations in the form of locations at which additional soil data sets should be collected by the sensor apparatus <NUM>. Such sampling recommendations may be provided to the operator via an interface, such as the display device <NUM> or to a system (e.g., an automated navigation or planning system) that may implement collection of additional soil data sets. In other examples, the sampling recommendations may include soil collection recommendations in the form of locations at which physical soil samples should be collected by the sampling mechanism <NUM> for laboratory analysis, as discussed in greater detail below. Such sampling recommendations may be provided to the operator via an interface, such as the display device <NUM>.

Additionally, and as functionally depicted in <FIG>, the sampling recommendations in the form of soil collection recommendations may be provided to a sampling mechanism <NUM> to implement collection of soil samples. Generally, the soil samples resulting from the soil collection recommendations may be analyzed according to laboratory testing to generate actual nutrient values. As such, and as described in greater detail below, a soil collection recommendation may be used to generate actual nutrient values for a location, which in turn, may be used to evaluate the estimated nutrient values and the associated location-based calibration curve for the respective location corresponding to the actual nutrient values.

In one example, the feedback module <NUM> receives at least a portion of the sensor-based soil data sets, the estimated nutrient values, at least a portion of the laboratory-based soil data sets, and the actual nutrient values. The feedback module <NUM> may also receive and/or consider the location-based calibration curves used to generate the estimated nutrient values. Generally, the feedback module <NUM> operates to evaluate the estimated nutrient values and the underlying location-based calibration curves in view of the actual nutrient values for a corresponding location. Ideally, the estimated nutrient values will be identical to the actual nutrient values. However, if the estimated nutrient values differ from the actual nutrient values, the feedback module <NUM> may modify or update the location-based calibration curves as location-based calibration curve updates, which may replace or modify one or more of the location-based calibration curves stored in data storage <NUM>.

As an example, reference is briefly made to <FIG>, which is an example plot <NUM> for potassium values with actual nutrient values for potassium represented on the horizontal axis and estimated nutrient values for potassium represented on the vertical axis. Ideally, the values would match one another, as represented by ideal line <NUM>. However, in this example, the values are skewed slightly such that a validation line <NUM> fit to the data of the plot <NUM> is offset relative to the ideal line <NUM>. Based on this type of feedback, the feedback module <NUM> may modify the calibration curves that generated the estimated nutrient values such that subsequent soil analysis results in estimated nutrient values better approximating the actual nutrient values, e.g. such that the validation line <NUM> is closer to the ideal line <NUM>.

The feedback module <NUM> may operate to update the location-based calibration curves in any suitable manner. In one example, the feedback module <NUM> may be supplemented or modified based on laboratory testing of soil samples from the field of the operator, as discussed above, and/or from other operators with similar soil types. Such updated calibration curves may be generated by neural network algorithms with machines learning techniques.

As appearing herein and generally referring to the feedback module <NUM>, the term "neural network" algorithm refers to a computer-readable program having a structure composed of multiple layers of interconnected nodes or neurons. The particular structure of a neural network algorithm (when employed) will vary between embodiments of the present disclosure, noting that several types of neural network algorithms currently exist and additional neural network types continue to be developed. Generally, a neural network algorithm may include an input layer into which data is fed (e.g., the estimated nutrient values generated from sensor-based soil data sets and actual nutrient values generated from laboratory-based soil data sets); a final output layer at which processing results appear; and any number of hidden layers between the input and output layers. Each node contained in a given layer of the neural network algorithm may be connected to some, if not all of the nodes in a subsequent network layer, thereby forming a processing structure loosely akin to a biological neural network. Additionally, the behavior or performance of a neural network algorithm may be modified by adjusting certain parameters associated with the nodes and connections of the neural network, including the activation strength or "weight" between node-to-node connections and, in many cases, an inactivity bias assigned to each node. Through iteratively modifying such parameters using feedback data, the neural network algorithm may be trained to improve the algorithm performance; that is, the tendency of the algorithm to provide a correct or desired result across a range input data sets. Such training may be considered "machine learning" when largely automated by providing the neural network algorithm with feedback data (which may be expressed using cost functions, as an example), with the neural network algorithm or an associated algorithm iteratively adjusting the network parameters (e.g., node-to-node weights and inactivity biases) without reliance or with a reduced reliance on direct human programming, to gradually improve the performance of the neural network algorithm.

By the nature of the neural network algorithm of the feedback module <NUM>, the accuracy of the estimated nutrient values should improve over time as additional soil samples are collected and as additional relationships between the various soil parameters discussed herein are recognized. The accuracy improvements should occur with respect to an individual field of the operator; and additionally, may occur with respect to other fields and other operators as the derived and recognized parameter relationships and more nuanced calibration curves are applied across a collective or wider soil analysis system <NUM>.

In any event, the updates to the calibration curves are provided to data storage <NUM> such that the analysis module <NUM> may access the updated location-based calibration curves for evaluation of subsequent sensor-based soil data sets, thereby improving the accuracy of the estimated nutrient values.

In some examples, the functions of the feedback module <NUM> may be performed by the soil analysis center <NUM> (<FIG>). In this manner, some determinations or conclusions gleaned from one operator or field may be considered with respect to other operators or fields. Similarly, the functions of one or more of the other modules <NUM>, <NUM>, <NUM> may be performed by the soil analysis center <NUM>.

Accordingly, the soil analysis system <NUM> provides relatively immediate and clear representations of soil characteristics for the operator. The soil analysis system <NUM> provides such representations for the particular field of the operator, including soil type maps, soil nutrient maps, nutrient recommendation maps, and sampling recommendations. Moreover, continued use of the soil analysis system <NUM> provides feedback that operates to improve the soil analysis. As a result, the soil analysis system <NUM> may provide an increased crop yield and more efficient use of time, water, and/or fertilizer, as well as decreases in human labor and financial costs for soil testing.

Although not shown, operation of the soil analysis system <NUM> discussed above may also be expressed as a method performing the operational steps in accordance with the present disclosure. Such methods may be implemented with respect to one or more vehicles in combination with a soil analysis center, a single vehicle, and/or the soil analysis center cooperating with one or more vehicles. As can be appreciated in light of the disclosure, the order of operation is not limited to a sequential execution described above, but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. Further one or more operational steps may be omitted and/or additional steps added.

A computer readable signal medium can include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium can be non-transitory and can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

As used herein, unless otherwise limited or modified, lists with elements that are separated by conjunctive terms (e.g., "and") and that are also preceded by the phrase "one or more of" or "at least one of" indicate configurations or arrangements that potentially include individual elements of the list, or any combination thereof. For example, "at least one of A, B, and C" or "one or more of A, B, and C" indicates the possibilities of only A, only B, only C, or any combination of two or more of A, B, and C (e.g., A and B; B and C; A and C; or A, B, and C).

As used herein, the term module refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. The term module may be synonymous with unit, component, subsystem, sub-controller, circuitry, routine, element, structure, control section, and the like.

In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of work vehicles.

Aspects of certain embodiments are described herein can be described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of any such flowchart illustrations and/or block diagrams, and combinations of blocks in such flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

Any flowchart and block diagrams in the figures, or similar discussion above, can illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block (or otherwise described herein) can occur out of the order noted in the figures. For example, two blocks shown in succession (or two operations described in succession) can, in fact, be executed substantially concurrently, or the blocks (or operations) can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of any block diagram and/or flowchart illustration, and combinations of blocks in any block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Claim 1:
A soil analysis system (<NUM>) for an agricultural vehicle (<NUM>), comprising:
a sensor apparatus (<NUM>) including a location sensor (<NUM>) configured to determine a location of the agricultural vehicle (<NUM>) and an infrared sensor (<NUM>, <NUM>) configured to collect infrared spectra from soil at the location;
a controller (<NUM>) coupled to the location sensor, to the infrared sensor (<NUM>, <NUM>) and to a display device (<NUM>) and/or to an actuator of a fertilizer applicator (<NUM>),
the controller (<NUM>) configured to determine a soil type based on the determined location; select at least one nutrient calibration curve based on the determined soil type at the determined location and analyze the infrared spectra according to the at least one selected nutrient calibration curve to generate at least one estimated nutrient value for the soil at the location; and
the controller (<NUM>) configured to generate display commands representing the at least one estimated nutrient value; wherein the display device (<NUM>) is configured to generate a display representing the at least one estimated nutrient value based on the display commands; and/or
the controller (<NUM>) configured to generate a fertilizer recommendation based on the first estimated nutrient value for the location; and to generate actuator signals for the fertilizer applicator (<NUM>) to selectively apply a fertilizer onto the field at the location based on the generated fertilizer recommendation.