Crop Prediction Engine Using Normalized Agronomic Field Data
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Solution Overview
Problem
Growers face challenges in optimizing crop productivity due to the large amount of information related to geographic, weather, agronomic, and environmental factors, which can be incomplete or analyzed imperfectly using existing crop production models.
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 map characteristics of plots of land and farming operations to expected crop productivity, thereby optimizing crop productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If growers use existing crop production models to analyze agricultural data, then they can make planting and harvesting decisions, but the analysis is imperfect due to the large and complex amount of information from multiple sources
Solution Approach 1:
The system segments the complex agricultural data analysis into distinct functional modules: data collection from multiple sources, data normalization, machine learning model training, and prediction generation. Each module handles a specific aspect of the analysis, making the overall complex system manageable and effective.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw agricultural data and decision-making. This intermediary processes and interprets the complex multi-source data, transforming it into actionable predictions about crop yields and optimal farming operations.
2Quantity of substance
If growers collect comprehensive information from multiple data sources to optimize crop production, then they have more complete data, but the quantity of information becomes so large as to limit utilization
Solution Approach 1:
The system extracts and normalizes key features from large volumes of heterogeneous agricultural data stored in a data lake. The machine learning model extracts relevant patterns and relationships from this extracted data, enabling effective utilization without being overwhelmed by the total data volume.
Solution Approach 2:
The patent transforms raw agricultural data into standardized normalized formats with consistent parameter structures. This parameter transformation enables the machine learning model to process diverse data sources uniformly, making comprehensive data usable for prediction and optimization.
3Productivity
If growers make decisions based on incomplete or imperfectly analyzed information, then they can act quickly, but crop productivity is suboptimal
Solution Approach 1:
The system performs preliminary actions by continuously training machine learning models on historical and current agricultural data before actual planting and harvesting decisions are needed. This pre-computed knowledge enables rapid, informed decision-making when actual farming operations require decisions, improving productivity without excessive decision time.
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.