Multi-dimensional Crop Response Surface for Yield Drivers
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Solution Overview
Problem
Current precision farming methods lack the ability to optimize soil nutrient applications and seed selection based on high-resolution soil characteristics and environmental factors, leading to suboptimal crop production due to incomplete or conflicting information, and a lack of focus on the dynamics of soil characteristics and crop responses.
Innovation Solution
A system and method using a crop prediction engine that generates a multi-dimensional crop response surface by analyzing soil composition data with machine learning models, including Random Forest and Generalized Additive Models, to predict optimal chemical, nutrient, and seed applications, and identify key drivers for crop yield in different soil regimes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If growers use traditional prescription recommendations from agronomists or suppliers, then they receive general guidance on farm applications, but the recommendations fail to optimize the amounts and types of applications for specific field conditions
Solution Approach 1:
The patent divides the field into multiple management zones based on soil characteristics, topography, and historical yield data. Each zone receives customized application rates rather than uniform treatment, enabling precise optimization of nutrient and seed applications for specific soil regimes while maintaining overall field productivity
Solution Approach 2:
The system changes multiple parameters simultaneously including soil organic matter content, texture classification, pH levels, and topographic position to create comprehensive soil regime classifications. These parameter changes enable the generation of zone-specific prescriptions that optimize crop production for each unique soil condition
2Measurement precision
If growers apply chemicals and nutrients at variable rates using GPS equipment, then they achieve sub-meter precision application, but they lack understanding of the underlying soil characteristics and crop response at such high-precision spatial resolutions
Solution Approach 1:
The system incorporates historical yield data, soil sample results, and application records as feedback to continuously refine management zone boundaries and prescription recommendations. This feedback loop ensures that high-precision applications are based on validated soil characteristic patterns and actual crop responses, preventing information loss
Solution Approach 2:
The patent creates nested layers of analysis from broad soil regimes down to specific management zones within fields. Each layer contains detailed soil characteristic information that is progressively refined, allowing growers to understand soil properties at multiple spatial scales simultaneously while maintaining accurate application precision
3Adaptability or versatility
If growers receive vast information about seed and fertilizer options from agronomists, then they have access to many possible applications, but the overwhelming quantity of information leads to incomplete or conflicting decisions
Solution Approach 1:
The system provides locally optimized recommendations for each management zone based on its specific soil regime characteristics. Instead of presenting all possible seed and fertilizer options across the entire field, it delivers targeted suggestions tailored to local conditions, reducing decision complexity while maintaining adaptability to varying soil types
Solution Approach 2:
The system changes the presentation parameters of information by organizing seed and fertilizer recommendations according to soil regime classifications. This parameter change transforms overwhelming detailed information into structured, zone-specific guidelines that are easier to process and implement
Data Source
AI summary
A system and method for visualizing one or more crop response surfaces. The system includes a geospatial database associated with a crop prediction engine. The geospatial database receives soil composition information for plots of land. The crop prediction engine identifies covariates from the soil composition information, which has a feature matrix. The crop prediction engine generates a multi-dimensional covariate training data set from the covariates. The crop prediction engine then applies the multi-dimensional covariate training data set to a machine learning training model to generate at least one predictive crop-yield predictive model. The crop prediction engine ranks covariates having feature set interactions. Subsequently, the crop prediction engine determines a dominant crop-yield feature set interaction from the ranked covariates having feature set interactions. The crop prediction engine generates a crop response surface from the dominant crop-yield feature set interaction. The crop prediction engine then visualizes the crop response surface.