{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:20:49Z","timestamp":1772720449432,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T00:00:00Z","timestamp":1630281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Modern Agricultural Industry Generic Key Technology Research and Development Innovation Team Project","award":["2020KJ133"],"award-info":[{"award-number":["2020KJ133"]}]},{"name":"Guangdong Science and Technology Plan Project","award":["2018A050506073"],"award-info":[{"award-number":["2018A050506073"]}]},{"name":"National Key Research and Development Program, grant number","award":["2018YFD0201506"],"award-info":[{"award-number":["2018YFD0201506"]}]},{"name":"the 111 Project","award":["D18019"],"award-info":[{"award-number":["D18019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatic acquisition of the canopy volume parameters of the Citrus reticulate Blanco cv. Shatangju tree is of great significance to precision management of the orchard. This research combined the point cloud deep learning algorithm with the volume calculation algorithm to segment the canopy of the Citrus reticulate Blanco cv. Shatangju trees. The 3D (Three-Dimensional) point cloud model of a Citrus reticulate Blanco cv. Shatangju orchard was generated using UAV tilt photogrammetry images. The segmentation effects of three deep learning models, PointNet++, MinkowskiNet and FPConv, on Shatangju trees and the ground were compared. The following three volume algorithms: convex hull by slices, voxel-based method and 3D convex hull were applied to calculate the volume of Shatangju trees. Model accuracy was evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE). The results show that the overall accuracy of the MinkowskiNet model (94.57%) is higher than the other two models, which indicates the best segmentation effect. The 3D convex hull algorithm received the highest R2 (0.8215) and the lowest RMSE (0.3186 m3) for the canopy volume calculation, which best reflects the real volume of Citrus reticulate Blanco cv. Shatangju trees. The proposed method is capable of rapid and automatic acquisition for the canopy volume of Citrus reticulate Blanco cv. Shatangju trees.<\/jats:p>","DOI":"10.3390\/rs13173437","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"3437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Yuan","family":"Qi","sequence":"first","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"}]},{"given":"Xuhua","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Rural and Biosystems Engineering, Chonnam National University, Gwangju 500-757, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3103-3254","authenticated-orcid":false,"given":"Pengchao","family":"Chen","sequence":"additional","affiliation":[{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"College of Electronic Engineering and College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Kyeong-Hwan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Rural and Biosystems Engineering, Chonnam National University, Gwangju 500-757, Korea"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"College of Electronic Engineering and College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Xiaoyang","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"}]},{"given":"Ruichang","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"}]},{"given":"Jizhong","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7795-9277","authenticated-orcid":false,"given":"Yali","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,30]]},"reference":[{"key":"ref_1","first-page":"98","article-title":"Review of the pesticide precision orchard spraying technologies","volume":"20","author":"Wang","year":"2004","journal-title":"Trans. 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