ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1655093
This article is part of the Research TopicInnovative Techniques for Precision Agriculture and Big DataView all 4 articles
Precision citrus segmentation and stem picking point localization using improved YOLOv8n-seg algorithm
Provisionally accepted
- 1Jianghan University, State Key Laboratory of Precision Blasting, Wuhan, China
- 2Jianghan University, Hubei Key Laboratory of Blasting Engineering, Wuhan, China
- 3Wuhan Academy of Agricultural Sciences, Wuhan, China
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Due to the small size of citrus stems, their color similarity to the background, and their variable position relative to the fruit, accurately locating picking points using robots in natural environments presents significant challenges. To address this issue, this study proposes a method for segmenting citrus fruits and stems based on an improved YOLOv8n-seg model, combined with geometric constraints for stem matching to achieve accurate localization of picking points. First, all standard convolutions in the model are replaced with GhostConv to reduce the number of model parameters. Furthermore, a convolutional block attention module (CBAM) and a small-object detection layer are introduced to enhance the model's feature representation and segmentation accuracy for small objects. Then, by incorporating the positional relationship between the fruit and the stem, constraints are defined to match the target stem, and an algorithm is designed to determine the optimal picking point. Experimental results show that the improved YOLOv8n-seg model achieves recall rates of 90.91% for fruits and stems, a mean average precision (mAP50) of 94.43%, and an F1-score of 93.51%. The precision rates for fruit and stem segmentation are 96.04% and 97.12%, respectively. The average detection rate of picking points reaches 88.38%, with an average localization time of 373.25 milliseconds under GPU support, demonstrating high real-time performance. Compared with other models, the improved YOLOv8n-seg model shows significantly better performance. This study confirms the reliability and effectiveness of the proposed citrus picking point localization method and lays a technical foundation for the automated harvesting of citrus fruits.
Keywords: Citrus, YOLOv8n-seg, Picking Point Localization, Instance segmentation, Picking robot
Received: 27 Jun 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Li, Yin, Zuo, Pan and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Han Li, Jianghan University, State Key Laboratory of Precision Blasting, Wuhan, China
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