AUTHOR=Shen Xiaojun , Shao Chaofan , Cheng Danyi , Yao Lili , Zhou Cheng 

TITLE=YOLOv5-POS: research on cabbage pose prediction method based on multi-task perception technology

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1455687

DOI=10.3389/fpls.2024.1455687

ISSN=1664-462X

ABSTRACT=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.