茶叶科学2024，Vol.44Issue(6)：949-959,11.
基于改进YOLOv8n的茶树嫩芽识别
Research on Tea Bud Recognition Based on Improved YOLOv8n
摘要
Abstract
Accurate recognition of tea buds in complex natural environment is one of the key technologies to realize intelligent picking of tea buds by agricultural robots.To address the problem of low recognition accuracy of tea buds in complex environment of tea gardens,a tea bud recognition method based on improved YOLOv8n was proposed.The Honor Mobile Phone was used to collect the RGB images of tea buds,and the image annotation of tea buds was completed.The labeled data was divided according to the 8∶1∶1 radio of the training set and test set.To effectively extract bud features and reduce model redundancy calculation and memory access,FasterNet was used to replace the backbone network of YOLOv8n model for feature extraction.To suppress the background information of the tea garden environment and enhance the feature extraction ability of tea buds,the global attention mechanism(GAM)module was introduced at the end of the backbone network(after the SPPF module).To further improve the recognition accuracy of tea buds,the Context Guided(CG)module was introduced into the Neck network to learn the joint features of local features and surrounding environment of tea buds.The improved YOLOV8n algorithm was trained and tested by using the constructed tea bud data set.The ablation experiments verify that the FasterNet network,GAM attention mechanism and CG module effectively improved the recognition accuracy of the YOLOv8n model.The mean average accuracy(mAP)of the improved YOLOv8n model on the multi-category tea bud data set was 94.3%.Compared with the original YOLOv8n model,the mAP of single bud,one bud and one leaf,and one bud and two leaves of tea buds increased by 2.2,1.6 and 2.7 percentage points,respectively.The improved YOLOv8n model was tested for performance comparison with YOLOv3-tiny,YOLOv3,YOLOv5m,YOLOv7-tiny,YOLOv7 and YOLOv8n models.The experimental results show that the improved YOLOv8n model has a higher accuracy in identifying tea buds.The experimental results demonstrate that the improved YOLOv8n model can effectively improve the accuracy of tea bud recognition and provide technical support for intelligent tea picking robots.关键词
深度学习/茶树嫩芽/图像识别/YOLOv8n/注意力机制/采摘机器人Key words
deep learning/tea buds/image recognition/YOLOv8n/attention mechanisms/picking robot分类
农业科技引用本文复制引用
杨肖委,沈强,罗金龙,张拓,杨婷,戴宇樵,刘忠英,李琴,王家伦..基于改进YOLOv8n的茶树嫩芽识别[J].茶叶科学,2024,44(6):949-959,11.基金项目
国家重点研发计划项目(2022YFD1600802、2021YFD1100305)、国家现代农业产业技术体系(CARS-19)、贵州省科技计划项目(黔科合支撑[2024]一般158)、贵州省茶叶产业技术体系(GZCYCYJSTX-05)、黔农科博士基金[2024]10 （2022YFD1600802、2021YFD1100305）
