Machine Learning Model for Crop Field Suitability Assessment

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Solution Overview

Problem

Agricultural entities face challenges in determining the suitability of crop fields for expansion due to numerous factors influencing crop yields, including climate, soil composition, plant diseases, and management practices, making the search process complex and daunting.

Innovation Solution

The implementation of a machine learning model that analyzes field-level agricultural management practices, climate data, soil composition, and crop yield data to determine the suitability of candidate crop fields for specific agricultural entities, using clustering and predictive analytics to identify compatible fields and provide personalized search results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual evaluation of crop fields is performed considering multiple factors (climate, soil, diseases, management practices), then assessment accuracy is improved, but the complexity and time required for field selection increases

Engineering Contradiction:

Improvefield suitability assessment accuracyVSAvoidsearch process complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces a machine learning model as an intermediary between the complex evaluation factors and the field selection decision. The model automatically processes multiple input features (climate data, soil composition, disease history, management practices) and generates a suitability score, eliminating the need for manual evaluation while maintaining comprehensive assessment accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The manual mechanical process of evaluating each field against multiple criteria is replaced with an automated computational system. The machine learning model substitutes human analysts and manual review processes, using algorithms to quickly assess field suitability based on the same comprehensive factors without the time and complexity burdens of manual evaluation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If comprehensive field evaluation considering multiple factors is conducted, then selection accuracy is improved, but the time required for field search increases

Engineering Contradiction:

Improvefield suitability assessment accuracyVSAvoidfield search time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and storing key field characteristics (climate data, soil composition, disease history) in a structured database before actual field selection is needed. When a field evaluation is required, the machine learning model can quickly retrieve and process this pre-organized data, significantly reducing the time required for comprehensive assessment while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The time-consuming manual evaluation process is replaced with automated machine learning computation. The model can assess multiple fields simultaneously and generate suitability scores in minutes rather than the days or weeks required for manual analysis, dramatically reducing search time while preserving comprehensive evaluation accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If generic field search criteria are used, then search simplicity is improved, but the relevance of results to specific agricultural entities decreases

Engineering Contradiction:

Improvesearch process simplicityVSAvoidresults relevance to entity practices

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent segments the field evaluation process into two independent components: (1) a generic machine learning model that processes field characteristics objectively, and (2) entity-specific management practice profiles that capture each agricultural entity's unique approaches. This segmentation allows the system to maintain search simplicity while incorporating customized relevance criteria specific to each entity's practices.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies local quality by tailoring the evaluation criteria to match specific agricultural entity characteristics. Instead of using uniform generic criteria for all entities, the model adjusts weighting and thresholds based on each entity's management practices, crop preferences, and operational style, ensuring results are locally optimized for each user while maintaining overall system simplicity.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11687960B2Analyzing crop fields based on agricultural management practices

Publication Date: 2023.06.27 DEERE & CO

AI summary

Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.