Detection of Jamming and Low Harvesting Height in Automated Cabbage Harvesting

Abstract

Agricultural labor shortages have increased the demand for automation in farming. In cabbage harvesting, automated 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 motion 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.