Probabilistic Crop Yield Modeling for Seed Density Decisions
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Solution Overview
Problem
Current digital models for agricultural yield prediction often lead to decreased crop yields due to point estimates, lacking the ability to effectively model crop yield based on multiple inputs and provide probabilistic estimates before planting, which can result in negative impacts on large-scale agricultural techniques across different fields.
Innovation Solution
A machine learning system is developed to compute parameters for a probability distribution of yield, using field-specific and crop-specific inputs to generate probabilistic estimates, which can then be used to select optimal seed types and densities, and control agricultural implements for precise planting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point estimates of agricultural yield are used through digital models, then the modeling process is simple and quick, but the reliability of yield prediction deteriorates leading to decreased crop yields
Solution Approach 1:
The patent transforms the yield prediction output from a single point estimate to a probability distribution characterized by multiple parameters (mean, standard deviation, skewness, kurtosis). This parameter expansion allows the model to capture yield uncertainty and variability while maintaining computational efficiency through parametric modeling approaches.
Solution Approach 2:
The invention adds a new dimension to yield prediction by transitioning from one-dimensional point estimates to multi-dimensional probability distributions. This dimensional expansion incorporates uncertainty quantification and risk assessment capabilities without substantially increasing model complexity through the use of parametric families.
2Reliability
If multiple inputs are used to model crop yield probabilistically, then the reliability of yield prediction improves, but the device complexity increases
Solution Approach 1:
The patent manages model complexity by parameterizing the probability distribution using a limited set of key parameters (mean, standard deviation, skewness, kurtosis) rather than modeling the full distribution non-parametrically. This parametric approach captures essential yield characteristics while maintaining computational tractability.
Solution Approach 2:
The probabilistic yield model serves multiple functions simultaneously: it provides point estimates for planning, uncertainty quantification for risk management, and distributional information for decision-making under uncertainty. This multi-functionality justifies the increased model complexity by delivering comprehensive yield information from a single modeling framework.
Data Source
AI summary
Systems and methods for improving the training of machine learning models to generate probability distributions of yield values are presented. In an embodiment, a system stores a machine learning system trained to compute parameters for a probability distribution of yield values based on seeding density, seed type, and information specific to a field. The system receives inputs for a particular field and computes parameters for a probability distribution of yield. The system generates a probability distribution of yield using the parameters and uses the probability distribution to generate a yield guarantee value. The system supplies the yield guarantee value to a field manager computing device with a seed type and/or seed density recommendation. When the system receives input accepting the recommendation, the system generates one or more scripts which, when executed by an application controller, causes the application controller to control an agricultural implement to cause the agricultural implement to plant a seed on the field according to the recommendation.