Variable Time Horizon Prediction Engine for Agricultural Control
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Solution Overview
Problem
Agricultural systems face inefficiencies due to limited time horizons in existing predictive models, leading to cumbersome and error-prone operations across current and future seasons, making it difficult to identify and remedy operational inefficiencies effectively.
Innovation Solution
A variable time horizon prediction engine that detects operational sensor inputs, receives optimization criteria, and applies control signals to improve agricultural system performance over selectable time horizons, using multiple models to predict performance across current operations, seasons, and future seasons, allowing operators to adjust parameters and visualize performance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single time horizon model is used for prediction, then the model complexity is low, but the system cannot identify inefficiencies across different time frames (current operation, current season, future seasons)
Solution Approach 1:
The system divides the prediction task into multiple time horizon models, each handling a specific time frame (current operation, current season, future seasons). This segmentation allows each model to specialize in its time horizon while the system as a whole provides comprehensive multi-timeframe analysis, resolving the contradiction between coverage and complexity.
Solution Approach 2:
The prediction system is designed to provide universal functionality across multiple time horizons. A single system architecture supports multiple models that can be selected based on the desired time horizon, making the system adaptable to different prediction needs without requiring separate systems for each time frame.
2Productivity
If multiple separate systems are used to control agricultural operations across different time horizons, then each time horizon can be optimized independently, but the system becomes cumbersome and error-prone
Solution Approach 1:
The system merges multiple time horizon models into a single integrated prediction system. This consolidation allows operators to access all time horizon predictions through one interface rather than managing multiple separate systems, reducing operational complexity while maintaining the ability to optimize across different time frames.
Solution Approach 2:
The system introduces a time horizon selection mechanism that acts as an intermediary between the operator and the multiple prediction models. This mediator allows the operator to select the appropriate time horizon model based on current needs, simplifying the interface while providing access to comprehensive prediction capabilities.
3Reliability
If existing predictive models with limited time horizons are used, then the model complexity is low, but operational inefficiencies cannot be identified and remedied effectively across different seasons and operations
Solution Approach 1:
The system implements dynamic model selection based on the desired time horizon. Rather than using a static single-model approach, the system dynamically adapts by selecting the appropriate time horizon model (current operation, current season, or future seasons) based on the prediction task, improving reliability while managing complexity through conditional model deployment.
Data Source
AI summary
A predictive engine obtains as inputs a set of optimization criteria and a time horizon selection input and applies those inputs to a time horizon model in a variable time horizon prediction engine. A user interface is generated that provides actuatable elements that can be actuated in order to access different time horizon models in the variable time horizon prediction engine. Operational parameters can be adjusted so that the variable time horizon prediction engine provides an output indicative of a control signal that can be used to improve operation of the agricultural system over the selected time horizon.