Agricultural Machine Control Using In-Situ Sensing to Prevent Coverage Gaps
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Solution Overview
Problem
Agricultural machines, such as combines and tillers, often experience coverage gaps due to inaccuracies in machine operation, leading to unharvested or untilled areas despite the use of automated control systems and imagery-based guidance, as delays or inaccuracies in actuating machine subsystems result in the header being raised or lowered too early or late.
Innovation Solution
A control system that analyzes image data from previous passes to generate performance metrics, such as coverage gaps, and adjusts machine settings for subsequent passes to prevent these gaps by identifying work control points and modifying settings like header positioning or speed to ensure complete coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated control systems and imagery-based guidance are used, then machine operation precision is improved, but coverage gaps still occur due to actuation delays
Solution Approach 1:
The system performs preliminary analysis of image data from previous passes to identify areas that were missed or under-covered. Based on this analysis, the control system proactively adjusts machine settings before entering the problematic areas in subsequent passes, preventing coverage gaps rather than reacting to them after they occur.
Solution Approach 2:
The system captures image data during and after each pass, analyzes the actual coverage achieved, and uses this feedback to automatically adjust machine settings for the next pass. This closed-loop feedback mechanism continuously improves coverage accuracy by learning from previous performance.
2Manufacturing precision
If machine settings are adjusted frequently to prevent coverage gaps, then coverage accuracy is improved, but system complexity increases
Solution Approach 1:
The control system automatically analyzes image data, generates performance metrics, determines optimal settings adjustments, and implements changes without operator intervention. The system serves itself by autonomously optimizing its own operation based on real-time feedback, reducing the need for complex manual control while maintaining high coverage accuracy.
3Manufacturing precision
If image data from previous passes is analyzed to adjust settings, then work quality is improved, but processing time increases
Solution Approach 1:
Image data is captured and analyzed during the machine operation itself, and performance metrics are generated in real-time. The system prepares adjustment recommendations ahead of time so that when the machine enters the next pass, the optimal settings are already determined and ready for immediate implementation, minimizing any processing delay.
Data Source
AI summary
A method of controlling a mobile agricultural machine that includes performing an agricultural operation during a given pass in a field using a first set of machine settings, obtaining in situ data representing the agricultural operation during the given pass, generating a performance metric based on the in situ data, identifying a second set of machine settings based on the performance metric, and outputting a control instruction that controls the mobile agricultural machine during a subsequent pass in the field based on the second set of machine settings.