Crop Prediction Models for Data-Driven Farming Operations
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Solution Overview
Problem
Growers face challenges in making informed planting, growing, and harvesting decisions due to the large volume of available information and incomplete understanding of geographic, weather-related, and environmental factors, leading to suboptimal crop production outcomes.
Innovation Solution
A system utilizing machine learning operations to normalize and format crop growth information into a unified format, apply prediction models to optimize farming operations, and provide modified sets of operations to enhance crop productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If growers utilize existing crop production models with large volume of information, then more data is available for decision making, but the quantity of information becomes so large as to limit the amount of information a grower can utilize
Solution Approach 1:
The system segments the large volume of crop production information into structured data categories (field characteristics, weather data, soil composition, planting strategies) that can be processed and analyzed systematically by machine learning models, making the information manageable and actionable for growers
Solution Approach 2:
The patent introduces machine learning models and data processing systems as intermediaries between the raw information and the grower. These intermediaries automatically analyze, synthesize, and present actionable insights, eliminating the need for growers to directly process overwhelming quantities of raw data
2Ease of operation
If growers make decisions based on incomplete information or imperfect understanding, then decision making is simplified, but crop production outcomes become suboptimal
Solution Approach 1:
The system enables self-service decision support by automatically collecting, analyzing, and presenting optimized planting and harvesting recommendations based on comprehensive data analysis. The machine learning models independently process information and provide actionable insights without requiring growers to have expert-level understanding of all underlying factors
Solution Approach 2:
The system implements feedback loops where crop production data is continuously collected, analyzed by machine learning models, and used to refine future recommendations. This ensures that decision-making accuracy improves over time while maintaining ease of use for growers
Data Source
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, 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.