ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1643700
This article is part of the Research TopicHighlights of 1st International Conference on Sustainable and Intelligent Phytoprotection (ICSIP 2025)View all 4 articles
YOLO-Lychee-advanced: An Optimized Detection M odel for Lychee Pest Damage Based on YOLOv11
Provisionally accepted
- Guangdong University of Petrochemical Technology, Maoming, China
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Abstract—We introduce YOLO-Lychee-advanced, a lightweig ht and high-precision detector for lychee stem-borer damage on f ruit surfaces. Built on YOLOv11, the model incorporates (i) a C2 f module with dual-branch residual connections to capture fine-g rained features of pest holes ≤2 mm, (ii) a CBAM channel-spatial attention block to suppress complex peel-texture interference, an d (iii) CIoU loss to tighten bounding-box regression. To mitigate i llumination variance, we augment the original 3,061-image datas et to 9,183 samples by simulating direct/back-lighting and adopt a "pest-hole only" annotation strategy, which improves mAP50-9 5 by 18% over baseline. Experiments conducted on an RTX 3060 with a batch size of 3 2 and an input size of 416 × 416 pixels show YOLO-Lychee-adva nced achieves 92.2% precision, 85.4% recall, 91.7% mAP50, and 61.6% mAP50-95, surpassing YOLOv9t and YOLOv10n by 3. 4% and 1.7%, respectively, while maintaining 37 FPS real-time s peed. Compared with the recent YOLOv9t and YOLOv10n basel ines on the same lychee test set, YOLO-Lychee-advanced raises mAP50-95 by 3.4 % and 1.7 %, respectively. Post-processing opti mization further boosts precision to 95.5%. A publicly available dataset and PyQt5 visualization tool are provided at https://githu b.com/Suxueping/Lychee-Pest-Damage-images.git. Index Terms—Lychee stem borer; Object detection; YOLOv11; Attention mechanism; Data augmentation
Keywords: Lychee stem borer, object detection, YOLOv11, attention mechanism, Data augmentation
Received: 09 Jun 2025; Accepted: 25 Sep 2025.
Copyright: © 2025 Wu, Su, Ma and Xu. 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:
Xianjun Wu, xianjun.wu@outlook.com
Bing Xu, xubing@gdupt.edu.cn
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