Predictive Crop State Mapping for Harvester Setting Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation when encountering varying crop states, heights, and terrain conditions, requiring frequent adjustments in machine settings to maintain optimal operation.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate functional maps that predict crop states, heights, and header characteristics, enabling automated control adjustments during harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated control adjustments are implemented using predictive maps, then harvesting efficiency is improved, but device complexity increases
Solution Approach 1:
The system generates predictive maps beforehand that forecast crop states, heights, and terrain conditions across the field. These maps are created using historical data and sensor information before the harvesting operation begins, allowing the control system to proactively adjust settings rather than reactively responding to conditions during harvesting. This preliminary preparation reduces the computational burden during actual operation while maintaining high harvesting efficiency.
Solution Approach 2:
The system creates simplified representative models (predictive maps) that copy and represent the complex physical conditions of the field. Instead of processing raw sensor data continuously during harvesting, the system uses these pre-generated map copies that capture essential field characteristics, reducing the complexity of real-time control decisions while preserving the ability to optimize harvesting operations.
2Ease of operation
If real-time sensor data processing is performed to generate predictive maps, then crop handling is improved, but loss of time occurs during data processing
Solution Approach 1:
The system performs data processing and predictive map generation before the harvesting operation begins. By preprocessing sensor data and creating predictive representations of field conditions in advance, the system eliminates time-consuming data processing during the actual harvesting operation, ensuring that crop handling optimizations are ready to be applied immediately without delay.
Solution Approach 2:
The system dynamically adjusts the level of processing based on operational needs. During harvesting, the system uses the pre-generated predictive maps for rapid decision-making, only performing additional processing when conditions change significantly or when updating maps is necessary, thus minimizing time loss while maintaining effective crop handling.
Data Source
AI summary
One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.