文献类型：期刊文献

英文题名：CountShoots: Automatic Detection and Counting of Slash Pine New Shoots Using UAV Imagery

作者：Hao, Xia[1] Cao, Yue[1] Zhang, Zhaoxu[1] Tomasetto, Federico[2] Yan, Weiqi[3] Xu, Cong[4] Luan, Qifu[5] Li, Yanjie[5]

第一作者：Hao, Xia

通信作者：Li, YJ[1]

机构：[1]Shandong Agr Univ, Coll Informat Sci & Engn, 61 Daizong Rd, Tai An 271018, Shandong, Peoples R China;[2]AgResearch Ltd, Christchurch 8140, New Zealand;[3]Auckland Univ Technol, Dept Comp Sci, Auckland 1010, New Zealand;[4]Univ Canterbury, Sch Forestry, Private Bag 4800, Christchurch 8041, New Zealand;[5]Chinese Acad Forestry, Res Inst Subtrop Forestry, 73 Daqiao Rd, Hangzhou 311400, Zhejiang, Peoples R China

年份：2023

卷号：5

外文期刊名：PLANT PHENOMICS

收录：;EI(收录号：20235015193565);Scopus(收录号：2-s2.0-85178904470);WOS:【SCI-EXPANDED(收录号:WOS:001124494500002)】；

基金：Acknowledgments Funding : This research was supported by Fundamental Research Funds of CAF, No. CAFYBB2022QA001 and the Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding, No. 2021C02070-7-3. Author contributions : X.H. conducted the experiments and wrote the manuscript. Y.C. provided the data collection, labeling, and analysis. Z.Z. con-structed and tested the application system. Y.L. designed the study, supported the data collection and field experiments, and revised the manuscript. F.T., W.Y., and C.X. performed the revi-sions of the manuscript, and all authors read and approved the final manuscript. Competing interests : The authors declare that they have no known competing financial interests or per-sonal relationships that could have appeared to influence the work reported in this study.

语种：英文

外文关键词：Aircraft detection - Antennas - Mean square error

摘要：The density of new shoots on pine trees is an important indicator of their growth and photosynthetic capacity. However, traditional methods to monitor new shoot density rely on manual and destructive measurements, which are labor-intensive and have led to fewer studies on new shoot density. Therefore, in this study, we present user-friendly software called CountShoots, which extracts new shoot density in an easy and convenient way using unmanned aerial vehicles based on the YOLOX and Slash Pine Shoot Counting Network (SPSC-net) models. This software mainly consists of 2 steps. Firstly, we deployed a modified YOLOX model to identify the tree species and location from complex RGB background images, which yielded a high recognition accuracy of 99.15% and 95.47%. These results showed that our model produced higher detection accuracy compared to YOLOv5, Efficientnet, and Faster-RCNN models. Secondly, we constructed an SPSC-net. This methodology is based on the CCTrans network, which outperformed DM-Count, CSR-net, and MCNN models, with the lowest mean squared error and mean absolute error results among other models (i.e., 2.18 and 1.47, respectively). To our best knowledge, our work is the first research contribution to identify tree crowns and count new shoots automatically in slash pine. Our research outcome provides a highly efficient and rapid user-interactive pine tree new shoot detection and counting system for tree breeding and genetic use purposes.