Residue Detection Control for Adaptive Agricultural Implements
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Solution Overview
Problem
Agricultural operations often result in residue coverage that is difficult to manage effectively, as existing technologies lack accurate detection and control methods to maintain desired residue levels, impacting operations like tillage and planting.
Innovation Solution
A system and method that utilize environmental and image data to detect residue coverage using image processing instructions, adjusting implement settings such as speed, depth, and tool angles to maintain optimal residue levels, incorporating sensors and cameras for real-time monitoring and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing is used to detect residue coverage, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system pre-stores multiple image processing methods in the controller before operation. When environmental conditions are detected, the controller automatically selects and applies the appropriate pre-prepared processing method, eliminating the need for complex real-time method development and reducing operational complexity while maintaining high measurement precision.
Solution Approach 2:
The system changes processing parameters by selecting different image processing methods based on environmental conditions (lighting, weather, crop type). This allows the same hardware to achieve high measurement precision across varying conditions without increasing device complexity, as the controller dynamically adjusts processing approach rather than adding hardware components.
2Productivity
If real-time monitoring with sensors and cameras is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The controller serves multiple functions: it processes image data, selects appropriate processing methods based on environmental sensors, determines residue coverage, and controls implement operations. This multi-functionality improves productivity by integrating monitoring and control in one system while managing complexity through consolidation rather than proliferation of separate systems.
Solution Approach 2:
The system implements continuous feedback by monitoring environmental conditions and residue coverage in real-time, then automatically adjusting implement operations. This feedback loop improves productivity by enabling dynamic optimization of operations while managing complexity through automated control algorithms that process sensor data and generate control signals.
3Measurement precision
If environmental data is collected and processed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
Multiple image processing methods are pre-stored in the controller, each optimized for specific environmental conditions. When operation begins, the system immediately detects environmental parameters and selects the appropriate pre-prepared method, eliminating time-consuming method development or selection during operation while maintaining high measurement precision through condition-matched processing.
Solution Approach 2:
The system replaces complex mechanical or manual residue assessment methods with automated image processing and environmental data analysis. This substitution reduces time loss by enabling rapid, automated determination of residue coverage based on sensor data and image analysis, while maintaining or improving measurement precision through objective, data-driven assessment.
Data Source
AI summary
A residue detection and implement control system and method are disclosed for an agricultural implement. The system includes a source of environment data and image data of an imaged area of a crop field containing residue. The system includes a data store containing a plurality of image processing methods and at least one controller that processes the image data according to one or more image processing instruction sets. The controller selects one or more of the image processing methods based on the environment data, and processes the image data using the selected image processing instruction(s) to determine a value corresponding to residue coverage in the imaged area of the field. The controller adjusts the configuration of the agricultural implement to respond to the amount and type of residue detected.