江苏农业学报2025，Vol.41Issue(1)：75-86,12.DOI:10.3969/j.issn.1000-4440.2025.01.010
一种基于改进YOLOv8n-seg的轻量化茶树嫩芽的茶梗识别模型
A lightweight model for identifying the stalks of tea buds based on the im-proved YOLOv8n-seg
摘要
Abstract
Identifying the stalks of tea buds is of great significance for achieving automated and intelligent tea picking.However,existing object detection algorithms face significant challenges in terms of low detection accuracy,high computation-al demands,and large model sizes,which collectively limit their deployment on edge devices.To address these challenges,we proposed a lightweight tea stalk detection model,YOLOv8n-seg-VLS,which was based on the YOLOv8n-seg framework.The model incorporated three significant enhancements.First,the VanillaNet lightweight module was introduced to replace traditional convolutional layers,thereby reducing the model's complexity.Second,a large separable kernel attention(LSKA)module was incorporated into the neck section of the network to minimize memory usage and resource consumption.Third,the loss function of YOLOv8 was modified from center intersection over union(CIoU)to shape-and scale-aware intersection over union(Shape-IoU),thereby enhancing the precision of bounding box localization.The experimental results on a collected tea dataset demonstrated that YOLOv8n-seg-VLS achieved a mean average precision(mAP)of 94.02%at mAP0.50 and 62.34%at mAP0.50∶0.95,with a precision of 90.08%and a recall of 89.96%.In com-parison to the original YOLOv8n-seg,the proposed model demonstrated an improvement in frame rate,reaching 245.20 frames per second(FPS).Moreover,the model size was 3.92 MB,which was only 57.39%of the size of YOLOv8n-seg.These re-sults provide technical support for further development of intelligent tea harvesting equipment.关键词
图像识别/茶叶采摘/轻量化模型/YOLOv8n-seg/VanillaNetKey words
image recognition/tea harvesting/lightweight model/YOLOv8n-seg/VanillaNet分类
信息技术与安全科学引用本文复制引用
施武,袁伟皓,杨梦道,许高建..一种基于改进YOLOv8n-seg的轻量化茶树嫩芽的茶梗识别模型[J].江苏农业学报,2025,41(1):75-86,12.基金项目
安徽省高校自然科学研究重点项目(KJ2020A0106) （KJ2020A0106）
安徽省重大科技专项(202103b06020013) （202103b06020013）
安徽省大学生创新创业计划项目(S202310364126) （S202310364126）
