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YOLOv8-SDC: An Improved YOLOv8n-Seg-Based Method for Grafting Feature Detection and Segmentation in Melon Rootstock Seedlings

文献类型： 外文期刊

作者： Li, Lixia 1 ; Gong, Kejian 1 ; Wang, Zhihao 2 ; Pan, Tingna 3 ; Jiang, Kai 2 ;

作者机构： 1.Kunming Univ Sci & Technol, Fac Modern Agr Engn, Kunming 650500, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

3.Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming 650500, Peoples R China

关键词： grafting robot; automated seedling-feeding mechanism; YOLOv8-SDC; cotyledon deflection angle; stem-cotyledon separation point

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 10 期

页码：

收录情况： SCI

摘要： To address the multi-target detection problem in the automatic seedling-feeding procedure of vegetable-grafting robots from dual perspectives (top-view and side-view), this paper proposes an improved YOLOv8-SDC detection segmentation model based on YOLOv8n-seg. The model improves rootstock seedlings' detection and segmentation accuracy by SAConv replacing the original Conv c2f_DWRSeg module, replacing the c2f module, and adding the CA mechanism. Specifically, the SAConv module dynamically adjusts the receptive field of convolutional kernels to enhance the model's capability in extracting seedling shape features. Additionally, the DWR module enables the network to more flexibly adapt to the perception accuracy of different cotyledons, growth points, stem edges, and contours. Furthermore, the incorporated CA mechanism helps the model eliminate background interference for better localization and identification of seedling grafting characteristics. The improved model was trained and validated using preprocessed data. The experimental results show that YOLOv8-SDC achieves significant accuracy improvements over the original YOLOv8n-seg model, YOLACT, Mask R-CNN, YOLOv5, and YOLOv11 in both object detection and instance segmentation tasks under top-view and side-view conditions. The mAP of Box and Mask for cotyledon (leaf1, leaf2, leaf), growing point (pot), and seedling stem (stem) assays reached 98.6% and 99.1%, respectively. The processing speed reached 200 FPS. The feasibility of the proposed method was further validated through grafting features, such as cotyledon deflection angles and stem-cotyledon separation points. These findings provide robust technical support for developing an automatic seedling-feeding mechanism in grafting robotics.

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