Blended Model for Agricultural Fertilizer Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current agricultural management systems fail to incorporate user preferences and tolerances into mathematical models for optimizing fertilizer distribution, leading to standalone recommendations that cannot be jointly utilized to maximize yield and minimize costs.
Innovation Solution
The implementation of an agricultural intelligence computer system that receives user tolerances and combines multiple statistical and process models using Bayesian updating to generate blended recommendations for fertilizer distribution, ensuring suggestions fall within user-defined bounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple standalone mathematical models are used for agricultural recommendations, then model prediction accuracy is improved, but the ability to integrate user preferences and provide joint recommendations deteriorates
Solution Approach 1:
The patent combines multiple standalone mathematical models (process-based model, statistical model, machine learning model) into a unified blended model framework. This merging allows the system to maintain the predictive accuracy of individual models while integrating them with user preferences and tolerances to provide comprehensive joint recommendations that address both model predictions and user-specific constraints.
Solution Approach 2:
The blended model framework serves multiple functions simultaneously: it processes inputs from different model types, incorporates user preferences and tolerances, generates optimized recommendations, and adapts to different agricultural scenarios. This multi-functionality resolves the contradiction by making the system both accurate (through multiple models) and adaptable (through user preference integration).
2Device complexity
If agricultural recommendations are generated without considering user tolerances, then model output simplicity is improved, but recommendation usability and farmer acceptance deteriorate
Solution Approach 1:
The system incorporates user-specific tolerances and preferences into the recommendation generation process. By allowing each user to define their own tolerance ranges for different commodities and fields, the system tailors the recommendations to local user needs and constraints, making them more usable and acceptable while maintaining model simplicity through standardized integration methods.
Solution Approach 2:
The blended model framework dynamically adjusts recommendations based on user-defined tolerances and preferences. The system can adapt the output to match user-specific constraints while maintaining the underlying model structure, thus preserving simplicity while enhancing usability through flexible, user-adapted recommendations.
Data Source
AI summary
In an embodiment, the techniques herein include receiving a request for a suggested distribution rate of a particular field-distributed commodity in a particular geographical area. Based on that request, two or more rate models for distribution of the particular field-distributed commodity are computed, where one rate model is a user-tolerance model. The suggested distribution rate of the particular field-distributed commodity is determined by performing Bayesian updating where the user-tolerance model is treated as a prior distribution and distributions for each of the other rate models of the two or more rate models for distribution of the particular field-distributed commodity in the particular geographical area are treated as input data in the Bayesian updating. The determined suggested distribution rate of the particular field-distributed commodity is then sent in response to the received request.