Predictive Crop Moisture Mapping for Harvester Feed Rate Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation when encountering areas with varying crop moisture due to differences in plant structure and soil conditions, leading to issues like increased feed rate, plugging, or grain loss.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate a functional predictive crop moisture map, which maps crop moisture values across a field, allowing for real-time adjustments in machine settings and operations to optimize harvesting performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates through fields with varying crop moisture, then the harvesting operation can proceed, but machine performance degrades due to differences in plant structure and soil conditions
Solution Approach 1:
The system dynamically adjusts harvester operating parameters (feed rate, ground speed, header height) based on real-time crop moisture conditions detected by sensors. The control system continuously modifies machine settings to adapt to varying field conditions, transforming the static harvester into a dynamic system that responds to environmental changes and maintains optimal performance across different moisture zones.
Solution Approach 2:
The system implements feedback control by using sensors to detect crop moisture content and plant structure characteristics, then feeding this information back to the control system which adjusts operating parameters accordingly. This closed-loop feedback mechanism enables the harvester to automatically compensate for varying field conditions, maintaining reliable performance without operator intervention.
2Productivity
If the feed rate is increased to maintain productivity, then harvesting efficiency improves, but plugging and grain loss increase in high moisture areas
Solution Approach 1:
The system applies local quality control by adjusting operating parameters specifically for different moisture zones within the field. In high moisture areas, the control system reduces feed rate and ground speed to prevent plugging, while in drier areas it increases these parameters to maintain productivity. This localized parameter adjustment ensures optimal performance in each specific field zone without compromising overall harvesting efficiency.
Solution Approach 2:
The system changes operating parameters (feed rate, ground speed, header height) based on detected crop moisture conditions. When high moisture is detected, the control system automatically reduces feed rate and ground speed to prevent plugging and grain loss. When moisture levels are lower, it increases these parameters to maintain productivity, thereby dynamically optimizing the harvesting process throughout the field.
3Reliability
If automated control adjustments are implemented based on predictive maps, then machine performance is optimized, but system complexity increases
Solution Approach 1:
The system implements self-service control by using onboard sensors to automatically detect crop moisture and plant structure characteristics, generating predictive moisture maps, and adjusting operating parameters without operator intervention. The harvester serves itself by autonomously adapting to field conditions, reducing the need for complex manual control systems and external monitoring equipment while maintaining optimized performance.
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.