Abstract: Automated grafting is an important means for modern agriculture to improve production efficiency and graft seedling quality, among which the use of visual systems to quickly segment target rootstock seedlings is the key technology to achieve automated grafting. This study aims to solve the problems of inaccurate image segmentation and slow detection speed in traditional rootstock seedling segmentation algorithms. To address these challenges, this study proposes a lightweight segmentation method based on an improved version of YOLOv8s-seg. The improved YOLOv8-seg introduces FasterNet as the backbone network and designs an RCAAM module to enhance feature extraction ability and lightweight model. The D-C2f module is improved to enhance feature fusion ability, achieving efficient and accurate segmentation of watermelon rootstock seedlings and improving grafting efficiency. This article designs a series of comparative experiments, comparing the improved version of YOLOv8-seg with classic models such as Unet, SOLO v2, Mask R-CNN, Deeplabv3+ on a test set containing watermelon rootstock seedlings, and evaluating the recognition performance and detection effect of the model. The experimental results show that the improved version of YOLOv8-seg outperforms other models in mAP coefficient index and can segment seedlings more accurately. This study provides reliable deep learning-based solution for the development of automatic grafting robots, which can effectively reduce labor costs and improve grafting efficiency, meeting the requirements of automated equipment for inference efficiency and hardware resources.

Qingcang Yu, Zihao Xu and Yi Zhu, “Watermelon Rootstock Seedling Detection Based on Improved YOLOv8 Image Segmentation” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160247

@article{Yu2025,
title = {Watermelon Rootstock Seedling Detection Based on Improved YOLOv8 Image Segmentation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160247},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160247},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Qingcang Yu and Zihao Xu and Yi Zhu}
}