Soil Covariate Interpolation for Precision Agriculture Zones
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Solution Overview
Problem
Current precision farming technologies lack effective methods to understand and manage soil characteristics at high spatial resolutions, leading to inefficient crop production due to incomplete information and conflicting recommendations, and there is a need for systems that can identify relationships between soil types and crop management practices.
Innovation Solution
A system and method that uses geospatial databases, machine learning models, and random forest algorithms to interpolate soil composition data, identify ground types, and provide recommendations for optimal seed selection and crop yield production by clustering similar soil types and predicting crop responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If precision farming equipment is used to apply chemicals and nutrients with sub-meter precision, then application accuracy is improved, but understanding of underlying soil characteristics at such high-precision spatial resolutions remains insufficient
Solution Approach 1:
The patent segments the field into numerous high-resolution spatial units (voxels) and groups them into management zones based on soil characteristics. This segmentation allows detailed soil analysis at sub-meter resolution while organizing the data into manageable zones for precision application equipment.
Solution Approach 2:
The patent replaces traditional mechanical soil sampling methods with machine learning models and remote sensing technologies. The system uses interpolated covariates and trained models to predict soil characteristics at high spatial resolutions without physical sampling at every location.
2Loss of information
If vast information about seed and fertilizer applications is provided to growers, then decision-making information is improved, but growers become overwhelmed by choices and conflicting recommendations
Solution Approach 1:
The patent merges multiple soil characteristics, covariates, and environmental factors into unified management zones with consolidated recommendations. Instead of presenting separate data for each parameter, the system integrates them into zone-level guidance that simplifies decision-making while maintaining comprehensive analysis.
Solution Approach 2:
The patent provides localized recommendations specific to each management zone rather than uniform field-wide advice. Each zone receives tailored seed and fertilizer recommendations based on its unique soil characteristics, giving growers precise local guidance without overwhelming them with field-level complexity.
3Measurement precision
If traditional soil sampling methods are used, then soil composition data is obtained, but spatial resolution is insufficient to match precision farming equipment capabilities
Solution Approach 1:
The patent performs preliminary soil sampling at strategic locations and uses machine learning models to interpolate and predict soil characteristics across the entire field. This preliminary sampling combined with computational modeling achieves high spatial resolution without requiring exhaustive physical sampling at every location.
Solution Approach 2:
The patent creates digital copies of soil characteristics through interpolated data and machine learning predictions. Instead of physically sampling every location, the system generates virtual soil profile copies based on limited physical samples and covariate relationships, achieving high resolution at reduced sampling effort.
Data Source
AI summary
A system and method for identifying ground types from one or more interpolated covariates. The method proceeds by accessing soil composition information for plots of land, in which the soil composition information includes measured soil sample results, environmental results, soil conductivity results or any combination thereof. The method continues by identifying covariates from the soil composition information. Subsequently, the method interpolates covariates associated with different locations with an interpolation training model. Voxels are generated that are each associated with interpolated covariates having a corresponding geographical location. The method trains a random forest training model with the interpolated covariates. The voxels traverse the trained random forest model to identify clusters of voxels that are co-associated. The method identifies a ground type by combining the co-associated clusters. Each ground type is associated with a crop zone, a soil fertility, or a farm management recommendation.