Implement State Monitoring Using Orientation Vectors and Soil Images
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Solution Overview
Problem
Automated farming operations lack effective monitoring of implement conditions, leading to potential damage and improper performance due to insufficient monitoring of wear and state, especially when operators are not directly observing the implements during automated navigation.
Innovation Solution
An implement management system that uses cameras and sensors to determine the state of implements by applying machine learning models to images and sensor data, modifying the vehicle's operating mode to prevent damage and ensure proper function, such as raising shanks before reversing to avoid breakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated farming operations are used to allow the operator to perform other tasks, then operator productivity is improved, but implement monitoring capability deteriorates
Solution Approach 1:
The implement management system enables the implement to monitor itself through integrated sensors that automatically detect wear, damage, and state changes without requiring operator intervention. The system self-diagnoses conditions by comparing sensor data against expected parameters and autonomously determines when intervention is needed.
Solution Approach 2:
The system continuously collects data from sensors monitoring implement orientation, force, and position, then provides real-time feedback to both the automated control system and the operator. This feedback loop enables the automated system to adjust operations based on actual implement conditions while keeping the operator informed without requiring constant manual inspection.
2Reliability
If the operator periodically checks the implement from the cabin, then implement state monitoring is maintained, but operator time and attention are consumed
Solution Approach 1:
The system replaces the mechanical approach of visual inspection with electronic sensing and automated data processing. Sensors continuously measure implement parameters and the control system automatically analyzes the data, eliminating the need for the operator to physically observe or manually check the implement while providing more consistent and accurate monitoring.
Solution Approach 2:
Instead of periodic discrete checks, the monitoring system operates continuously, constantly collecting and analyzing implement data. This continuous monitoring provides uninterrupted surveillance of implement conditions without requiring the operator to periodically divert attention from other tasks, thereby eliminating time loss while maintaining comprehensive monitoring.
3Reliability
If the operator manually monitors implement wear and state, then damage prevention is possible, but operational efficiency decreases
Solution Approach 1:
The system performs preliminary detection of wear and damage conditions by continuously comparing sensor measurements against expected parameters. It identifies potential problems before they develop into critical failures, allowing preventive maintenance to be scheduled at convenient times rather than forcing operational interruptions. The system predicts when wear will reach critical levels and alerts the operator in advance.
4Productivity
If automated operations run without monitoring, then productivity increases, but implement damage risk increases
Solution Approach 1:
The implement management system acts as an intermediary between the automated operation and the implement, serving as a protective layer that monitors conditions and communicates status to both the control system and operator. This intermediary function enables automated operations to proceed while maintaining damage prevention capabilities, as the system can alert operators to conditions requiring intervention without stopping productive operations.
Data Source
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AI summary
An implement management system detects implement wear and monitors implement states to modify operating modes of a vehicle. The system can determine implement wear using the pull of the implement on the vehicle, the force and angle of which is represented by an orientation vector. The system may measure a current orientation vector and determine an expected orientation vector using sensors and a model (e.g., a machine learned model). Additionally, the implement management system can determine an implement state based on images of the soil and the implement captured by a camera onboard the vehicle during operation. The system may apply different models to the images to determine a likely state of the implement. The difference between the expected and current orientation vectors or the determined implement state may be used to determine whether and how the vehicle's operating mode should be modified.