Deep Learning Soybean VPI Characterization for Objective Variety Selection
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Solution Overview
Problem
Conventional methods for characterizing soybean varieties are subjective, labor-intensive, and rely on unreliable intuition, making it difficult for growers and advisors to determine suitable planting strategies based on field-specific conditions.
Innovation Solution
A computing system using machine learning models, particularly deep learning neural networks, to analyze machine data and predict variety profile index (VPI) values, enabling objective characterization of soybean plants and informing field management recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual characterization methods are used, then detailed soybean variety analysis can be performed, but extensive manual labor and time are required
Solution Approach 1:
The patent replaces manual mechanical characterization methods with machine learning models that process satellite imagery and environmental data. The system uses automated algorithms to predict soybean variety performance metrics, eliminating the need for manual field measurements and sample analysis while maintaining or improving characterization accuracy.
Solution Approach 2:
The patent introduces satellite imagery and environmental data as intermediary inputs between the field conditions and variety characterization. These intermediaries carry information about soil moisture, temperature, and other environmental factors that influence soybean growth, allowing the ML model to predict variety performance without direct manual measurement.
2Adaptability or versatility
If conventional subjective evaluation methods are used, then grower intuition can guide variety selection, but reliability and reproducibility are compromised
Solution Approach 1:
The patent transforms subjective grower intuition into objective quantitative parameters by using ML models to process environmental data and predict variety-specific metrics such as soil moisture response and temperature tolerance. This allows variety selection to be based on measurable, consistent parameters rather than subjective judgment.
Solution Approach 2:
The system incorporates feedback loops where predicted variety performance metrics are continuously refined based on actual field outcomes. The ML model learns from historical data and adjusts predictions to improve reliability while maintaining adaptability to different field conditions.
3Quantity of substance
If extensive manual sampling is performed, then comprehensive field data can be collected, but resource requirements and operational complexity increase
Solution Approach 1:
The patent extracts the essential information needed for variety characterization from satellite imagery and environmental data sources, eliminating the need for extensive physical sampling in the field. The system takes out only the critical data elements required for accurate prediction while discarding unnecessary manual collection steps.
Solution Approach 2:
The patent uses multi-functional satellite imagery that simultaneously provides information about soil moisture, vegetation health, and environmental conditions. This universal data source replaces multiple specialized sampling tools and methods, reducing system complexity while maintaining comprehensive data collection.
Data Source
AI summary
A system includes one or more processors; and one or more non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to: receive a machine data set; process the machine data set with a trained deep learning model to generate predicted variety profile index values; and cause a visualization to be displayed.