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

Lixia Li, Kejian Gong, Zhihao Wang, Tingna Pan and Kai Jiang ()
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Lixia Li: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Kejian Gong: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Zhihao Wang: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Tingna Pan: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Kai Jiang: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Agriculture, 2025, vol. 15, issue 10, 1-20

Abstract: 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.

Keywords: grafting robot; automated seedling-feeding mechanism; YOLOv8-SDC; cotyledon deflection angle; stem–cotyledon separation point (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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