农业机械学报2026，Vol.57Issue(13)：127-139,13.DOI:10.6041/j.issn.1000-1298.2026.13.009
基于YOLO v5n-DRSW的机采茶青芽叶形态智能检测研究
Intelligent Morphological Detection Research of Mechanically Picked Tea Green Buds and Leaves Based on YOLO v5n-DRSW
摘要
Abstract
Accurate morphological detection of mechanically harvested fresh tea leaves is beneficial for improving the accuracy and efficiency of automated grading.YOLO v5n-DRSW,an advanced YOLO v5n-based model specifically designed for the precise recognition of machine-harvested tea leaf morphologies was introduced.The model integrated several innovative features:a distribution shifting convolution(DSConv)module in the head network to reduce complexity and enhance efficiency;a reparameterized generalized feature pyramid network(RepGFPN)in the neck to improve generalization and robustness;and the squeeze-and-excitation(SE)attention mechanism embedded in the backbone to strengthen feature perception for small targets like tender buds.By leveraging a global field of view,this mechanism further enhanced the perception capability of the feature maps.Additionally,the wise intersection over union(WIoU)loss function was used to dynamically adjust gradient contributions during training.Compared with the baseline,YOLO v5n-DRSW exhibited significant advantages in detecting machine-harvested tea leaves with complex morphologies.Experimental results demonstrated that YOLO v5n-DRSW achieved 98.1%accuracy,a 2.1 percentage points improvement over the baseline,with an inference time of just 2.11 ms per frame.This rapid processing speed represented a notable improvement over the baseline model.The model also reduced floating-point operations by 2.44%,confirming its lightweight nature.In practical applications,it attained an average online recognition accuracy of 94.34%with a miss rate below 1.1%,highlighting its strong potential for enhancing automated tea leaf grading systems.Overall,the model demonstrated excellent detection performance,providing reliable assistance for improving the morphological detection outcomes of fresh tea leaves.关键词
机采茶青/卷积神经网络/注意力机制/特征金字塔网络/YOLO v5n/形态检测Key words
mechanically picked tea/convolutional neural network/attention mechanism/feature pyramid network/YOLO v5n/form realization分类
农业科技引用本文复制引用
郭嘉明,王建业,夏红玲,郭鹏,丁志武,刘妍华..基于YOLO v5n-DRSW的机采茶青芽叶形态智能检测研究[J].农业机械学报,2026,57(13):127-139,13.基金项目
广东省科技计划项目(2023B0202120001)和高水平农科院建设专项(NYQS202612) （2023B0202120001）
