YOLOv5-POS: research on cabbage pose prediction method based on multi-task perception technology - Shen, Xiaojun; Shao, Chaofan; Cheng, Danyi; Yao, Lili; Zhou, Cheng (Frontiers in Plant Science, 2024)
Accurate and rapid identification of cabbage posture is essential for mechanical harvesters to minimize damage to cabbage heads during the harvesting process. This study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bé zier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem. Validated on a multi-variety cabbage image dataset, YOLOv5-POS achieves an F1 score of 98.8% for head and root detection, with an average instance segmentation accuracy of 93.5%. The average absolute error and average relative error in cabbage pose recognition are recorded as 1.38° and 2.32°, respectively. The prediction model achieves an average accuracy of 98%, with cabbage posture recognition requiring only 28 milliseconds. This method effectively supports real-time cabbage harvesting.