{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:32:14Z","timestamp":1772303534818,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61675003"],"award-info":[{"award-number":["No. 61675003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province","award":["No. 2019KZDZX1012"],"award-info":[{"award-number":["No. 2019KZDZX1012"]}]},{"name":"the University Student Innovation Cultivation Program of Guangdong, China","award":["No. pdjh2019b0079"],"award-info":[{"award-number":["No. pdjh2019b0079"]}]},{"name":"the Key-Area Research and Development Program of Guangdong Province","award":["No. 2019B020214003"],"award-info":[{"award-number":["No. 2019B020214003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Instance segmentation of fruit tree canopies from images acquired by unmanned aerial vehicles (UAVs) is of significance for the precise management of orchards. Although deep learning methods have been widely used in the fields of feature extraction and classification, there are still phenomena of complex data and strong dependence on software performances. This paper proposes a deep learning-based instance segmentation method of litchi trees, which has a simple structure and lower requirements for data form. Considering that deep learning models require a large amount of training data, a labor-friendly semi-auto method for image annotation is introduced. The introduction of this method allows for a significant improvement in the efficiency of data pre-processing. Facing the high requirement of a deep learning method for computing resources, a partition-based method is presented for the segmentation of high-resolution digital orthophoto maps (DOMs). Citrus data is added to the training set to alleviate the lack of diversity of the original litchi dataset. The average precision (AP) is selected to evaluate the metric of the proposed model. The results show that with the help of training with the litchi-citrus datasets, the best AP on the test set reaches 96.25%.<\/jats:p>","DOI":"10.3390\/rs13193919","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Deep Learning-Based Instance Segmentation Method of Litchi Canopy from UAV-Acquired Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Jiawei","family":"Mo","sequence":"first","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongzi","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbin","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-3443","authenticated-orcid":false,"given":"Xiaoling","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1080\/01431161.2017.1410300","article-title":"What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?","volume":"39","author":"Hunt","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","first-page":"1","article-title":"Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing","volume":"1","author":"Lan","year":"2019","journal-title":"Smart Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ampatzidis, Y., and Partel, V. (2019). UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence. Remote Sens., 11.","DOI":"10.3390\/rs11040410"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1007\/s11119-020-09777-5","article-title":"Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery","volume":"22","author":"Osco","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1038\/s41438-021-00560-9","article-title":"Applications of deep-learning approaches in horticultural research: A review","volume":"8","author":"Yang","year":"2021","journal-title":"Hortic. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Csillik, O., Cherbini, J., Johnson, R., Lyons, A., and Kelly, M. (2018). Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. Drones, 2.","DOI":"10.3390\/drones2040039"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7500","DOI":"10.1080\/01431161.2019.1569282","article-title":"Young and mature oil palm tree detection and counting using convolutional neural network deep learning method","volume":"40","author":"Mubin","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1038\/s41598-020-79653-9","article-title":"Explainable identification and mapping of trees using UAV RGB image and deep learning","volume":"11","author":"Onishi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s13735-020-00195-x","article-title":"A survey on instance segmentation: State of the art","volume":"9","author":"Hafiz","year":"2020","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zamboni, P., Junior, J.M., Silva, J.D.A., Miyoshi, G.T., Matsubara, E.T., Nogueira, K., and Gon\u00e7alves, W.N. (2021). Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images. Remote Sens., 13.","DOI":"10.3390\/rs13132482"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Morales, G., Kemper, G., Sevillano, G., Arteaga, D., Ortega, I., and Telles, J. (2018). Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning. Forests, 9.","DOI":"10.3390\/f9120736"},{"key":"ref_15","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, Springer International Publishing."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_17","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). YOLACT: Real-time Instance Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., and Li, L. (2020, January 23\u201328). SOLO: Segmenting Objects by Locations. Proceedings of the Computer Vision\u2014ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., and Yan, Y. (2020, January 13\u201319). BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1080\/2150704X.2020.1784491","article-title":"Tree extraction from multi-scale UAV images using Mask R-CNN with FPN","volume":"11","author":"Ocer","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Akiva, P., Dana, K., Oudemans, P., and Mars, M. (2020, January 13\u201319). Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00033"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Marques, P., P\u00e1dua, L., Ad\u00e3o, T., Hru\u0161ka, J., Peres, E., Sousa, A., and Sousa, J.J. (2019). UAV-Based Automatic Detection and Monitoring of Chestnut Trees. Remote Sens., 11.","DOI":"10.3390\/rs11070855"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dong, X., Zhang, Z., Yu, R., Tian, Q., and Zhu, X. (2020). Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard. Remote Sens., 12.","DOI":"10.3390\/rs12010133"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Timilsina, S., Aryal, J., and Kirkpatrick, J.B. (2020). Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN). Remote Sens., 12.","DOI":"10.3390\/rs12183017"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106273","DOI":"10.1016\/j.compag.2021.106273","article-title":"Computer vision-based citrus tree detection in a cultivated environment using UAV imagery","volume":"187","author":"Donmez","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105500","DOI":"10.1016\/j.compag.2020.105500","article-title":"Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees\u2019 crowns from UAV multispectral imagery","volume":"175","author":"Modica","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2019.12.010","article-title":"A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery","volume":"160","author":"Osco","year":"2020","journal-title":"ISPRS J. Photogramm."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, Y., Hou, C., Tang, Y., Zhuang, J., Lin, J., He, Y., Guo, Q., Zhong, Z., Lei, H., and Luo, S. (2019). Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment. Sensors, 19.","DOI":"10.3390\/s19245558"},{"key":"ref_29","unstructured":"Epperson, M. (2018). Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems, University of California."},{"key":"ref_30","first-page":"252","article-title":"Litchi Flower and Leaf Segmentation and Recognition Based on Deep Semantic Segmentation","volume":"52","author":"Xiong","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"164546","DOI":"10.1109\/ACCESS.2020.3021739","article-title":"Semantic Segmentation of Litchi Branches Using DeepLabV3+ Model","volume":"8","author":"Peng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., and Gupta, A. (2017, January 22\u201329). Revisiting Unreasonable Effectiveness of Data in Deep Learning Era. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.97"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3390\/agriengineering2020019","article-title":"Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing","volume":"2","author":"Deng","year":"2020","journal-title":"AgriEngineering"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context, Springer International Publishing.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1109\/83.951537","article-title":"New Edge-Directed Interpolation","volume":"10","author":"Li","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","unstructured":"Etten, A.V. (2018). You Only Look Twice: Rapid Multi-Scale Object Detection in Satellite Imagery. arXiv."},{"key":"ref_39","unstructured":"Jia, D., Wei, D., Socher, R., Li, L.J., Kai, L., and Li, F.F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Computer Vision & Pattern Recognition, Miami, FL, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3919\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:07:52Z","timestamp":1760166472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,30]]},"references-count":40,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193919"],"URL":"https:\/\/doi.org\/10.3390\/rs13193919","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,30]]}}}