文献类型：期刊文献

英文题名：Research on Tea Trees Germination Density Detection Based on Improved YOLOv5

作者：Wang, Jinghua[1];Li, Xiang[1];Yang, Guijun[2];Wang, Fan[1,2];Men, Sen[3,4];Xu, Bo[2];Xu, Ze[5];Yang, Haibin[5];Yan, Lei[1]

第一作者：Wang, Jinghua

通讯作者：Wang, F[1];Yan, L[1];Wang, F[2]

机构：[1]Beijing Forestry Univ, Sch Technol, 35 Tsinghua East Rd, Beijing 100083, Peoples R China;[2]Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China;[3]Beijing Union Univ, Coll Robot, Beijing 100020, Peoples R China;[4]Beijing Union Univ, Beijing Engn Res Ctr Smart Mech Innovat Design Ser, Beijing 100020, Peoples R China;[5]Tea Res Inst, Chongqing Acad Agr Sci, Chongqing 402160, Peoples R China

第一机构：Beijing Forestry Univ, Sch Technol, 35 Tsinghua East Rd, Beijing 100083, Peoples R China

通讯机构：[1]corresponding author), Beijing Forestry Univ, Sch Technol, 35 Tsinghua East Rd, Beijing 100083, Peoples R China;[2]corresponding author), Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China.

年份：2022

卷号：13

期号：12

外文期刊名：FORESTS

收录：;EI(收录号：20225213314632);Scopus(收录号：2-s2.0-85144615933);WOS:【SCI-EXPANDED(收录号:WOS:000902720900001)】；

基金：This research was funded by the Fundamental Research Funds for the Central Universities (BLX202126); the Opening Foundation of Key Lab of State Forestry Administration on Forestry Equipment and Automation under Grant (BFUKF202220); the Chongqing Technology Innovation and Application Development Special Project (cstc2021jscx-gksbX0064); Qingyuan Smart Agriculture Research Institute + New R&D Insititutions Construction in North and West Guangdong (2019B090905006); and the General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China under Grant (KM201911417008).

语种：英文

外文关键词：improved YOLOv5; Xception; ShuffleNetV2; receptive field block; tea bud recognition; germination density

摘要：Tea plants are one of the most widely planted agricultural crops in the world. The traditional method of surveying germination density is mainly manual checking, which is time-consuming and inefficient. In this research, the Improved YOLOv5 model was used to identify tea buds and detect germination density based on tea trees canopy visible images. Firstly, five original YOLOv5 models were trained for tea trees germination recognition, and performance and volume were compared. Secondly, backbone structure was redesigned based on the lightweight theory of Xception and ShuffleNetV2. Meanwhile, reverse attention mechanism (RA) and receptive field block (RFB) were added to enhance the network feature extraction ability, achieving the purpose of optimizing the YOLOv5 network from both lightweight and accuracy improvement. Finally, the recognition ability of the Improved YOLOv5 model was analyzed, and the germination density of tea trees was detected according to the tea bud count. The experimental results show that: (1) The parameter numbers of the five original YOLOv5 models were inversely proportional to the detection accuracy. The YOLOv5m model with the most balanced comprehensive performance contained 20,852,934 parameters, the precision rate of the YOLOv5m recognition model was 74.9%, the recall rate was 75.7%, and the mAP_0.5 was 0.758. (2) The Improved YOLOv5 model contained 4,326,815 parameters, the precision rate of the Improved YOLOv5 recognition model was 94.9%, the recall rate was 97.67%, and the mAP_0.5 was 0.758. (3) The YOLOv5m model and the Improved YOLOv5 model were used to test the validation set, and the true positive (TP) values identified were 86% and 94%, respectively. The Improved YOLOv5 network model was effectively improved in both volume and accuracy according to the result. This research is conducive to scientific planning of tea bud picking, improving the production efficiency of the tea plantation and the quality of tea production in the later stage.