Crop Prediction Models for Farming Operation Optimization

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Solution Overview

Problem

Growers face challenges in optimizing crop productivity due to the vast amount of geographic, weather-related, agronomic, and environmental factors that can limit the utilization of available information for decision-making.

Innovation Solution

A system that normalizes crop growth information, trains a crop prediction engine using machine learning operations, and applies it to field information to optimize crop productivity by identifying the best set of farming operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If growers utilize existing crop production models with available information, then decision-making can be supported, but the vast quantity of information limits the amount that can be effectively utilized

Engineering Contradiction:

Improveutilization of available informationVSAvoidcomplexity of information processing

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical/manual information processing methods with machine learning operations. The system automatically processes vast quantities of geographic, weather-related, agronomic, and environmental factors using trained machine learning models, eliminating the need for growers to manually analyze and synthesize this complex information while still utilizing all available data effectively

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces machine learning models as intermediaries between the vast quantity of available agricultural information and the grower's decision-making process. These models act as mediators that automatically synthesize, analyze, and translate complex multi-source data into actionable planting, growing, and harvesting recommendations, reducing the cognitive burden on growers while maximizing information utilization

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If growers make decisions based on incomplete information or imperfect understanding, then decisions can be made quickly, but crop productivity is reduced

Engineering Contradiction:

Improvecrop productivityVSAvoidtime for information analysis

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent performs preliminary action by pre-training machine learning models on historical crop growth information from multiple data sources before the actual decision-making moment. This advance preparation enables the system to quickly provide optimized farming operation recommendations without requiring growers to spend time analyzing raw data, thus both improving crop productivity through better decisions and reducing the time needed for information analysis

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentEP4508962A1Machine learning in agricultural planting, growing, and harvesting contexts

Publication Date: 2025.02.19 INNOVATION ASSET COLLECTIVE

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AI summary

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, can optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.