Crop Prediction Engine Using ML for Land-Specific Farm Decisions
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Solution Overview
Problem
Agricultural producers face challenges in optimizing crop productivity due to incomplete and imperfect information about various geographic, weather-related, and environmental factors, limiting their ability to make informed decisions about planting, growing, and harvesting strategies.
Innovation Solution
A system that normalizes crop growth information from disparate sources and uses machine learning operations to train a crop prediction engine, which maps land characteristics and farming operations to expected crop productivity, providing optimized farming operations to enhance productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If growers utilize existing crop production models with available information, then decision-making capability is improved, but the completeness and accuracy of information utilization deteriorates due to the overwhelming quantity of data
Solution Approach 1:
The system segments the overwhelming quantity of agricultural data into distinct categories (geographic factors, weather-related factors, environmental factors, agronomic factors) and processes them separately through machine learning models, allowing comprehensive information utilization without overwhelming the decision-making process
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the raw data and the grower's decision-making process. These models automatically process, analyze, and synthesize the complex multi-source data, transforming it into actionable insights without requiring the grower to directly manage the overwhelming information volume
2Loss of time
If growers make decisions based on incomplete information, then decision-making speed is improved, but crop production optimization deteriorates
Solution Approach 1:
The system performs preliminary analysis of geographic, weather, environmental, and agronomic factors using machine learning models before the grower makes planting decisions. By pre-processing the complex data and generating predictive insights in advance, the system enables fast decision-making without sacrificing production optimization
Solution Approach 2:
The machine learning models automatically self-process the complex multi-source data without requiring manual intervention from the grower. The system independently analyzes the information, generates predictions, and provides recommendations, enabling rapid decision-making while maintaining comprehensive optimization through continuous automated analysis
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.