Machine Learning Models for Location-Specific Crop Price Forecasting
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Solution Overview
Problem
Existing machine learning models for predicting commodity prices are not tailored for farmers and fail to account for location-specific nuances, leading to inaccurate predictions for crop planning, as they are designed for commodity traders and do not consider basis price history at individual elevators.
Innovation Solution
The use of multiple machine learned models that continually learn from data to predict agricultural commodity prices at the crop and farm level, incorporating crop yield, operational costs, and storage costs, with a system that includes an in-memory database, data intelligence application, and robotic basis price automation to provide up-to-date forecasts, and a selection model to choose the best prediction based on root mean square error and trend analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models designed for commodity traders are used to predict agricultural commodity prices, then the models can provide general price forecasts, but they fail to account for location-specific nuances and basis price history at individual elevators, leading to inaccurate predictions for crop planning
Solution Approach 1:
The patent applies local quality by training separate machine learning models for different geographic locations and elevators, allowing each model to learn location-specific patterns in basis price history. This enables the system to account for local nuances in commodity pricing while maintaining overall system effectiveness across multiple regions.
Solution Approach 2:
The patent segments the pricing prediction system into multiple independent machine learning models, each dedicated to a specific elevator or location. This segmentation allows each model to specialize in local patterns without being constrained by generalizations from other regions, thereby improving prediction accuracy for crop planning decisions.
2Measurement precision
If multiple machine learned models are used to predict prices at crop and farm level with location-specific data, then prediction accuracy improves, but system complexity increases due to multiple models, databases, and integration requirements
Solution Approach 1:
The patent implements a universal architecture where multiple location-specific machine learning models share common data infrastructure, training protocols, and prediction interfaces. This multi-functional design allows the system to handle diverse location-specific predictions while maintaining standardized operations, thereby managing complexity through systematic reuse of components.
Solution Approach 2:
The patent introduces an intermediary layer that coordinates between multiple location-specific models and the user interface. This mediator manages model selection, aggregates predictions, and presents unified results to farmers, thereby shielding users from the underlying complexity of multiple models while preserving prediction accuracy.
Data Source
AI summary
In an example embodiment, multiple machine learned models are used to continually learn from data to update various prediction models. Prediction of item prices, at the production and distribution point level, and utilizing this information along with other item information, such as crop yield, operational cost, and storage cost in the case of arming, may be used to solve the long- and short-term planning problems of farmers and help in decision making based on daily and the most up-to-date forecasts.