Agricultural Work Machine Crop Flow Monitoring
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Solution Overview
Problem
Agricultural work machines, such as combine harvesters, face inefficiencies due to the challenge of detecting crop jams in a timely manner, leading to interruptions and reduced harvesting efficiency, as existing monitoring systems rely heavily on precise image processing for optical flow analysis which may not accurately capture directional changes in crop flow.
Innovation Solution
The implementation of a control device with both a velocity characteristic map and a directional change characteristic map, generated by an image processing unit, to improve the monitoring accuracy and reliability of crop flow, allowing for earlier detection of potential jams by analyzing pixel shifts and directional changes within the crop collection arrangement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optical flow analysis is used to monitor crop flow, then crop jam detection capability is improved, but measurement precision deteriorates due to difficulty in distinguishing crop flow from machine part movements
Solution Approach 1:
The patent divides the image processing into two separate analysis streams: one for detecting machine part movements (using edge detection and template matching) and another for analyzing crop flow (using optical flow on segmented crop regions). This segmentation allows the system to process different types of motion separately, improving the accuracy of crop flow velocity measurements by excluding machine part movement interference.
Solution Approach 2:
The patent introduces an intermediary processing step that separates crop regions from machine parts in the image sequence. By using color segmentation and edge detection as intermediary processes, the system identifies and isolates crop areas before applying optical flow analysis, thereby preventing machine part movements from contaminating the crop flow velocity measurements.
2Reliability
If the driver visually monitors crop flow continuously, then crop jam detection reliability is improved, but productivity deteriorates due to harvesting interruptions
Solution Approach 1:
The patent implements a self-monitoring system where the machine automatically detects crop jams using optical flow analysis and control algorithms. The system continuously analyzes crop flow velocity and automatically triggers alerts or control actions without requiring driver intervention, thereby maintaining high harvesting efficiency while ensuring reliable crop jam detection.
Solution Approach 2:
The patent establishes a feedback loop where the optical flow analysis results are fed back to the control device in real-time. When crop flow velocity drops below a threshold indicating a potential jam, the system provides feedback to the driver or automatically adjusts machine operations, enabling continuous monitoring without harvesting interruptions.
3Measurement precision
If image processing is enhanced for accurate optical flow analysis, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the image processing task into distinct modules: edge detection for machine part identification, color segmentation for crop region isolation, and optical flow analysis for velocity calculation. This modular segmentation improves measurement precision while managing device complexity by organizing processing functions into separate, manageable components.
Solution Approach 2:
The patent extracts and removes the machine part movement component from the overall image analysis by using edge detection and template matching to identify and exclude machine parts. This extraction allows the optical flow analysis to focus solely on crop flow, improving measurement precision without requiring overly complex all-encompassing processing algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This dual-characteristic map approach enhances the timeliness and accuracy of crop flow monitoring, reducing misinterpretations and enabling proactive control measures to prevent crop jams, thereby improving harvesting efficiency by distinguishing between crop flow and machine part movements.
Implementation Method 1
at least one sensor unit for optically detecting a crop flow
Implementation Method 2
the sensor device generates an image sequence, i.e., a plurality of consecutive images. According to the prior art, every two consecutive images are grouped to form an image pair, and positional shifts of intensity patterns (pixels or groups of pixels) between the images of the respective image pair are determined
Data Source
AI summary
An agricultural work machine has a crop collection arrangement for separating and collecting crops from field vegetation, which crop collection arrangement has at least one crop cutting device, a crop conveying device arranged downstream thereof and a crop intake device arranged downstream thereof, and having a control device which has at least one sensor unit for optically detecting a crop flow, an image processing unit for processing images which are generated by the sensor unit based on the optically detected crop flow, and a data output unit for displaying the images processed by the image processing unit. The image processing unit generates a velocity characteristic map and a directional change characteristic map based on the images generated by the sensor unit. The two characteristic maps are utilized jointly or each individually by the control device to control processes in the agricultural work machine and/or in the crop collection arrangement.