Predictive Moisture Mapping for Stable Harvester Operation
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Solution Overview
Problem
Agricultural harvesters face performance degradation when encountering areas of varying crop moisture due to changes in crop structure and moisture content, leading to issues such as increased feed rate, plugging, or grain loss.
Innovation Solution
Generate a functional predictive crop moisture map using in-situ data combined with historical and geographic data to control agricultural machines, adjusting settings like header height and feed rate to optimize performance across varying field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates at high speed through fields, then productivity increases, but performance degrades when encountering areas of varying crop moisture leading to plugging and grain loss
Solution Approach 1:
The harvester implements dynamic adjustment of operating parameters (feed rate, header height, reel speed) based on real-time crop moisture detection. The system transitions from static fixed-speed operation to dynamic variable-speed operation, allowing the machine to adapt its feed rate and other parameters continuously as it moves through areas of varying crop moisture, thereby maintaining reliable operation at high overall productivity
Solution Approach 2:
The system incorporates moisture sensors that continuously detect crop moisture levels and feed this information back to the control system. This feedback loop enables real-time adjustment of operating parameters to prevent plugging and grain loss, allowing the harvester to maintain both high speed and operational stability by responding to actual field conditions
2Reliability
If the harvester adjusts settings frequently to accommodate varying field conditions, then operational reliability improves, but device complexity increases
Solution Approach 1:
The harvester employs self-adjusting mechanisms where moisture sensors automatically detect crop moisture levels and trigger appropriate adjustments in feed rate and other parameters without requiring manual operator intervention. The control system autonomously processes sensor data and implements parameter changes, reducing the need for complex manual control interfaces while maintaining operational reliability
Solution Approach 2:
The system focuses on adjusting key operational parameters (feed rate, header height, reel speed) based on moisture detection rather than redesigning the entire control architecture. By concentrating adjustments on these critical parameters, the system achieves improved reliability through a relatively simple control mechanism that modifies existing system parameters rather than adding complex new subsystems
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.