Predictive Field Mapping for Harvester Reel Position Control
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Solution Overview
Problem
Agricultural machines face performance degradation due to varying vegetation heights across fields, as existing technologies lack effective methods to adapt machine settings in real-time to optimize harvesting efficiency.
Innovation Solution
An agricultural work machine generates predictive maps using in-situ sensors and prior data to control reel position and other subsystems, allowing for real-time adjustments based on vegetation height and crop conditions, thereby enhancing harvesting performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If machine settings are kept fixed for simplicity, then device complexity is reduced, but harvesting performance degrades due to varying vegetation heights
Solution Approach 1:
The patent implements dynamic machine settings that automatically adjust harvesting parameters based on real-time vegetation height data from predictive maps and in-situ sensors. The control system continuously modifies reel position, header height, and other settings to match actual field conditions, transforming the static configuration into an adaptive system that maintains optimal performance across varying crop heights.
Solution Approach 2:
The system generates predictive maps of vegetation height before harvesting operations begin, allowing the control system to pre-calculate optimal machine settings for upcoming field sections. This preliminary action enables the harvester to proactively adjust settings rather than reactively responding to changing conditions, improving harvesting efficiency while maintaining manageable system complexity.
2Adaptability or versatility
If real-time sensor data processing is implemented, then adaptability to varying vegetation heights is improved, but device complexity increases
Solution Approach 1:
The patent employs feedback mechanisms where in-situ sensors continuously measure actual vegetation height during harvesting and compare it against predictive map data. The control system uses this feedback to automatically adjust machine settings, creating a closed-loop system that adapts to real-time conditions. This feedback-driven approach enhances adaptability while keeping control complexity manageable through automated algorithms.
Solution Approach 2:
The control system performs self-adjustment of harvesting parameters based on sensor data and predictive maps, eliminating the need for constant manual intervention. The system autonomously processes sensor inputs, compares them with target values from predictive maps, and automatically modifies machine settings, thereby improving adaptability without proportionally increasing operational complexity.
3Measurement precision
If predictive maps are generated using multiple data sources, then measurement precision of vegetation characteristics is improved, but loss of time in data processing increases
Solution Approach 1:
The system generates predictive maps of vegetation height and other characteristics before harvesting operations begin, consolidating data from multiple sources (satellite imagery, historical yield data, soil maps) in advance. This preliminary processing allows the harvester to access pre-computed, high-precision vegetation data during field operations without experiencing real-time processing delays, thus maintaining measurement precision while minimizing time loss during actual harvesting.
Solution Approach 2:
The large-scale data processing task is segmented into manageable portions corresponding to different field sections or control zones. The system processes and generates predictive maps for specific geographic areas in advance, allowing parallel processing and reducing overall computation time. During harvesting, the system only needs to access and apply pre-processed data for the current location, significantly reducing real-time data processing requirements while maintaining high measurement precision.
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.