Detection of Jamming and Low Harvesting Height in Automated Cabbage Harvesting
Masaki Asano, Takanori Fukao
AI summary
Problem
Automated cabbage harvesters relying on side-mounted cameras frequently fail due to outer leaf occlusion, leading to harvesting height errors and cabbage jamming.
Approach
The system combines overhead camera tracking with an LSTM network for jamming detection and LiDAR point cloud trajectory analysis to identify low harvesting height, triggering automatic height adjustments.
Key results
- 95.3% accuracy in jamming detection
- 95% accuracy in low harvesting height detection
- Real-time operation at 10 Hz in field tests
- 97.0% overall control accuracy preventing severe blockages
Why it matters
Enables more reliable and robust automated cabbage harvesting, addressing critical agricultural labor shortages by reducing machine failures in the field.
Abstract
Agricultural labor shortages have increased the demand for automation in farming. In cabbage harvesting, au- tomated harvesters rely on a side-mounted camera for detection to control harvesting height, but occlusion from outer leaves can cause errors and lead to failures. This paper presents a robust detection and control framework that integrates YOLO- based cabbage detection, trajectory tracking, LSTM-based motion classification, and LiDAR point cloud analysis. The system functions as a fail-safe while also providing redundancy, enabling recovery when side-mounted camera detection fails, and addresses two critical failure modes: cabbage jamming during extraction and low harvesting height. Temporal mo- tion features are classified by an LSTM, while LiDAR-based trajectory analysis of the cabbage head point cloud centroid identifies low harvesting height. When both jamming and low harvesting height are detected, the system issues a raising command to the harvester. Experiments on real-world data demonstrated 95.3% accuracy in jamming detection and 95% in low harvesting height detection. Field experiments confirmed real-time operation at 10 Hz and effective prevention of severe blockages, achieving an overall control accuracy of 97.0%. These results demonstrate the feasibility of the proposed method for robust automated cabbage harvesting.