Patent Application
20230082714
The present invention relates to a method of predicting soil properties, for example nutrient levels, in unknown regions or zones of an agricultural field using a model that uses current soil test data of nearby regions or zones within the same agricultural field as an input.
Precision agronomists often attempt to divide a field or agricultural area into “management zones”—geographic divisions which are predicted to have roughly homogeneous soil characteristics and yield potential. Taking soil samples from each zone then allows fertilizer prescriptions to be tailored to that zone, optimizing production and economic return for the farmer. Taking soil samples is labor intensive and can be both costly and time consuming when required for an agricultural field having many large sized zones.
According to one aspect of the invention there is provided a method of predicting soil nutrient levels for a current growing season in a common agricultural field having a plurality of regions including at least one first region having a current soil test value that is known from an actual soil test and at least one second region having a current soil test value that is unknown, the method comprising:
According to a second aspect of the present invention there is provided a system comprising one or more processors and one or more memories storing computer program instructions for predicting soil nutrient levels for a current growing season in a common agricultural field having a plurality of regions including at least one first region having a current soil test value that is known from an actual soil test and at least one second region having a current soil test value that is unknown, the system, when executing the computer program instructions by the one or more processors, being configured to:
It is possible to reduce the cost of soil sampling by instead modeling the soil properties in each zone, using e.g., a process-based or machine-learning-based modeling approach that considers previous seasons' soil sample properties and the weather, fertilizer, cropping and yield history of that zone. Here we propose a method of improving the soil-property modeling accuracy, by using actual soil test results from one management zone as a predictor when modeling other zones' properties.
The system and/or method may further include training the soil test model using a machine learning algorithm with soil test values and field specific characteristics associated with a plurality of different training agricultural fields at different stages throughout one or more growing seasons.
The system and/or method may further include training the soil test model using data from a training agricultural field having a plurality of regions including at least one known region in which a soil test value is known from an actual soil test and at least one unknown region in which the soil test value is unknown, by assigning a virtual value as the soil test value for said at least one unknown region based upon the soil test value of said at least one known region.
The step of assigning the virtual value as the soil test value for said at least one unknown in the system and/or method may further comprise (i) ranking the known regions according to productivity, and (ii) using the soil test value of a median region among the ranked known regions as the virtual test value assigned to the soil test value for said at least one unknown region.
The step of assigning the virtual value as the soil test value for said at least one unknown in the system and/or method may comprise (i) ranking the known regions according to productivity and (ii) using the soil test value of a median region among the ranked known regions as one of the predictors for estimating the soil test value for the unknown regions.
The step of training the soil test model in the system and method may further comprise assigning the soil test value of the median region as the soil test value for each known region. In this instance, the step of training the soil test model may further comprise assigning the soil test value from one of the known regions adjacent to the median region as the soil test value for the median region.
The system or method may be further arranged such that for each known region having data from a plurality of soil tests associated therewith, an average value is calculated from said data and the average value is assigned as the soil test value for that known region.
The known field specific characteristics may include: (i) the nutrient levels acquired from soil tests performed during the prior growing season, (ii) weather data relating to common agricultural field during either or both of the current growing season and the prior growing season, (iii) soil characteristics other than nutrient levels, measured in-field during either or both of the current growing season and the prior growing season, (iv) remotely sensed data acquired during either or both of the current growing season and the prior growing season, (v) harvest layer information associated with either or both of the current growing season and the prior growing season, (vi) yield values associated with either or both of the current growing season and the prior growing season, (vii) agronomist recommendations associated with either or both of the current growing season and the prior growing season, (viii) fertilizer applications associated with either or both of the current growing season and the prior growing season, and/or (ix) any combination of the above characteristics.
The step of providing the soil test model in the system and/or method may further comprise (i) selecting one or more field specific characteristics among a plurality of field characteristics available for the training agricultural field using an embedded feature selection approach, and (ii) training the soil test model to define said statistical relationship using the selected field specific characteristics.
The system or method may be further arranged such that for each first region of the common agricultural field having data from a plurality of actual soil tests associated therewith, an average value is calculated from said data and the average value is assigned as the current soil test value for that first region.
The Figures (FIGS.) and the following description relate to preferred embodiments by way of illustration only. It should be noted that from the following discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that may be employed without departing from the disclosed principles. It is noted that wherever practicable similar or like reference numbers may be used in the figures and may indicate similar or like functionality. The figures depict embodiments of the disclosed system (or method) for purposes of illustration only.
One embodiment of the invention will now be described in conjunction with the accompanying drawings in which:
In the drawings like characters of reference indicate corresponding parts in the different figures.
According to the present invention, a standard Virtual Soil Testing (VST) model uses machine learning techniques to derive statistical relationships between a zone's previous-season agronomic details (previous-season soil test results, weather, cropping, harvest, applications, etc.) and the current season's soil properties. The improved model leverages the zoning process that we already use; realizing that soil properties measured in one zone can be helpful in predicting properties for other zones in the same field. The new model considers the same predictors as the standard model, but additionally considers the current-season soil properties measured in other zones in the same field and could also consider properties of that zone relative to the one being predicted for. The new relationships learned by the machine learning process includes information about the relationships between zones' properties, resulting in more accurate predictions whenever a soil sample from a nearby zone is available. Operationally, this means we can scale back from sampling every zone in a field, to just sampling one and using the result to enhance the predictions in the other zones.
An overview of the processes includes:
Training
(i) Take all zones in a management zone with actual soil tests (ASTs).
(ii) Select the median zone number in which zone numbers are in order of increasing quality. So, this is selecting the median quality zone.
(iii) Also select the median zone number+1.
(iv) Create the infield AST feature by: (a) For every zone except the median zone, the infield value is the AST from the median zone; and (b) For the median zone, the infield value is the median zone number+1.
Prediction
(i) Expect that each management zone has at least one AST.
(ii) Use the average of the ASTs as the infield AST feature.
VST System Environment 100
Field data 110 can be data acquired from (a) weather stations 111 (data can include, for example, precipitation, daily and hourly precipitation, temperature, wind gust, wind speed, pressure, clouds, dew point, delta T, GFDI, relative humidity, historical weather, forecast, wind direction, barometric pressure, growing degree days, humidity), (b) sensor probes 112 (for example, a soil moisture probe that provides near-real-time data on volumetric soil moisture content, which gets converted into percent available water within the crop rooting zone, inches of available water, crop root dynamics, and irrigation requirements; the probe also measures soil temperature at various depths), (c) soil samples 113 (data can include, for example, elemental composition, pH, organic matter, cation exchange capacity, percent base saturation, excess lime, soluble salts), and (d) remote sensors 114 (for example, sensors on farm structures, drones, and robots).
Data collected in the data repository 130 is processed to derive value from data that can drive functions such as visualization, reports, decision making, and other analytics. Functions created may be shared and/or distributed to authorized users and subscribers. The processing of data occurs in data modeling and analytics 120. Some authorized users or devices may be given authorization to only view the data stored in the data repository 130, not change it. Other authorized users or devices may be given authorization to both view/receive data from and transmit data into the data repository 130.
Data modeling and analytics 120 may be programmed or configured to manage read operations and write operations involving the data repository 130 and other functional elements of a precision agricultural system. For example, in
The VST module 200 collects all these data from data repository 130 to run the VST process. If certain criteria are met, the VST module will generate predictions for the requested fields using the trained model so that the results can be transmitted to the authorized user.
VST Module 200
A goal of precision farming is to improve site-specific agricultural decision making through collection and analysis of data, formulation of site-specific management recommendations, and implementation of management practices to correct the factors that limit crop growth, productivity, and quality (Mulla and Schepers, 1997). Management zones are used in precision farming to divide a field or agricultural area into geographic divisions which are predicted to have homogenous soil properties and fertility levels. The process of “zoning” a field presents an opportunity to physically sample one zone and use the results to make better virtual samples of the other zones. This gives a nice balance between cost savings (reducing the number of physical samples) and accuracy of the virtual samples. Methods and systems in this disclosure improve the soil-property modeling accuracy, by using actual soil test results from one zone along with field-specific data, previous season soil test results, recommended fertilizer, crop and yield history, etc. as predictors when modeling other zones' soil properties.
The difference between the standard VST approach and the new VST model is depicted in
Data Fetching 210
Data fetching module 210 collects various field-specific features and weather data from the data repository 130. Data from various sources such as weather stations 111, sensor probes 112, remote sensors 114, etc. are stored in data repository 130. For generating features for the VST method, field-specific properties such as fertilizer, cropping and yield history, soil sample properties, the weather data and previous-season agronomic details are collected from the data repository 130 by the data fetching module 210.
Examples of data fetched for the VST methods can be as follows: (i) Weather data (including daily and hourly precipitation, temperature, wind gust, wind speed, wind pressure, wind direction, cloud percentage, dew point, relative humidity, barometric pressure, solar radiation, and relative humidity); (ii) Crop information (variety, previous crops, seeding date, etc.); (iii) Regional soil characteristics; (iv) Previous and current season Soil test results; (v) Agronomist recommendation (for example, applied fertilizer amount); and (vi) Yield and harvest history.
Preprocessing Module 220
In the pre-processing module 220, the data collected in the data fetching module 210 is processed to make them ready for further processing. A series of filters are applied to the dataset to remove the anomalous data. There are few preprocessing steps such as null value imputation, one-hot encoding, dropping highly correlated features, feature engineering, etc. are applied to the input data in this module.
Zone Selection 230
For an in-field VST method, a zone needs to be selected to use the feature from that zone to generate the in-field feature for the other zones within a management zone. The zone numbers within a management zone are in order of increasing order. All the zone numbers within a management zone with actual soil test (AST) results are collected to select the median zone number and the median zone number+1 to use the properties from those zones as the AST feature for the other zones.
In-Field AST Feature Generation 240
In-field AST feature generation is the process of generating features from the current season soil properties from a zone to generate features for other zones in the same field. For every zone except the median zone, the infield value is the AST from the median zone selected from the zone selection module 230. And, for the median zone, the infield value is the median zone number+1. For example, for subfields with two zones, the infield AST feature values are the swapped values of the ASTs and for subfields with three zones, the infield AST feature for zones 1 and 3 is the value of zone 2's AST. The infield AST feature for zone 2 is the value of zone 3's AST.
Feature Selection 250
For selecting the best features for the VST method, a feature selection approach is adopted. Before feeding the features to a machine learning model, the important features are selected by using the feature selection module 250. There are various feature selection algorithms available in the literature such as filter method, wrapper method, embedded method, etc. In one example embodiment of the VST method, embedded feature selection approach is used to select the important features for the VST model.
Machine Learning Module 260
Machine learning module 260 is a process-based or machine-learning-based modeling approach that considers previous seasons' soil sample properties and the weather, fertilizer, cropping and yield history of that zone. The in-field AST is an improvement over the standard VST approach by using actual soil test results from one management zone as a predictor when modeling other zones' properties. All these features are fed to a machine learning model to interpret the relationships between a zone's previous-season agronomic details and the current-season soil properties. A few examples of commonly used algorithms that can be used in this machine learning module 260 for interpreting the relationship are eXtreme Gradient Boosting (XGBoost), Neural Network or any tree-based algorithm.
Model Training
For training the VST model, the selected features are fed to the machine learning model. A 5-fold cross-validation is performed to select the best-fitted model. The accuracy of the models is evaluated using the mean absolute error (MAE).
Prediction
When predicting the soil properties for a field or management zone, all the field-specific features, weather data, crop and yield history are pulled from the data repository 130. For generating the in-field AST feature, it is expected that each management zone will have at least one actual soil test result. In the case of more than one AST, the average of all the ASTs is considered as the infield AST feature. The features selected in the model training are taken from the list of all the features and fed to the trained model to generate the prediction of the soil properties for the current season.
Once the prediction of the soil properties has been determined, it may be accessed by the grower or authorized third-party entities. Furthermore, this information may be sent to the grower or authorized third-party entities in the form of a notification.
Since various modifications can be made in this invention as herein above described, and many apparently widely different embodiments of same made, it is intended that all matter contained in the accompanying specification shall be interpreted as illustrative only and not in a limiting sense.
This application claims the benefit under 35 U.S.C. 119(e) of U.S. provisional application Ser. No. 63/243,263, filed Sep. 13, 2021.
| Number | Date | Country | |
|---|---|---|---|
| 63243263 | Sep 2021 | US |
