Corn Growth Efficiency Modeling for Objective Field Recommendations
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Solution Overview
Problem
Conventional methods for characterizing corn growth efficiency are subjective and unreliable, leading to ineffective hybrid selection and management recommendations in agricultural fields.
Innovation Solution
A machine learning-based system that determines corn growth efficiency (CGE) values using agronomic data, including soil and environmental factors, to provide objective and data-driven management recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional subjective methods are used to characterize corn growth, then ease of operation is improved, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent replaces subjective human observation and conventional mechanical measurement methods with a machine learning-based computational system. The ML model processes agronomic data (soil properties, weather, management practices) to objectively quantify corn growth efficiency, eliminating the subjectivity inherent in conventional methods while maintaining operational feasibility through automated data processing.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw agronomic data and growth efficiency characterization. The ML model serves as a mediator that transforms multiple input data features into standardized CGE values, providing an objective bridge between observable field conditions and growth efficiency assessment.
2Reliability
If machine learning models are implemented to improve measurement precision, then reliability of growth characterization is improved, but device complexity increases
Solution Approach 1:
The patent develops a universal machine learning model that can characterize corn growth efficiency across multiple fields, hybrids, and environmental conditions. The single ML model serves multiple functions: processing various agronomic data types, evaluating different corn hybrids, and providing consistent CGE rankings across diverse growing conditions, thereby reducing the need for multiple specialized systems.
Solution Approach 2:
The patent transforms complex agronomic data into standardized CGE parameter values that range from 1-10. By converting diverse input features (soil properties, weather patterns, management practices) into a unified standardized output parameter, the system simplifies complexity while maintaining reliability through consistent parameter transformation.
3Measurement precision
If comprehensive agronomic data is collected to improve measurement precision, then reliability of CGE values is improved, but loss of time and resource consumption increase
Solution Approach 1:
The patent utilizes agronomic data that is already collected and stored from routine field operations, soil testing, and weather monitoring. By leveraging pre-existing data infrastructure and collecting data as part of normal farming operations rather than dedicated research activities, the system achieves high measurement precision without significant additional time investment.
Solution Approach 2:
The machine learning model automatically processes and analyzes agronomic data without requiring manual field measurements or additional resource-intensive data collection activities. The system serves itself by utilizing readily available digital data from modern agricultural operations, eliminating the need for separate data collection expeditions.
Data Source
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AI summary
Example machine learning (ML) methods and systems for characterizing corn growth efficiency (CGE), and generating field management recommendations based on CGE values are disclosed. An example computing system includes one or more processors, and storage media. The media storing an ML model trained using a training agronomic data set labeled with one or more known CGE values corresponding to one or more trial agricultural fields. The media further storing instructions that, when executed, cause the system to: obtain a production agronomic data set corresponding to a target agricultural field; determine one or more input feature vectors based on the production agronomic data set; process the one or more input feature vectors, with the ML model, to generate one or more predicted CGE values for one or more portions of the target agricultural field; and provide the one or more predicted CGE values as an output.