{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:08:27Z","timestamp":1773716907163,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["2019B020221001"],"award-info":[{"award-number":["2019B020221001"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["D18019"],"award-info":[{"award-number":["D18019"]}]},{"name":"the Key Field Research and Development Plan of Guangdong Province, China","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"the Key Field Research and Development Plan of Guangdong Province, China","award":["2019B020221001"],"award-info":[{"award-number":["2019B020221001"]}]},{"name":"the Key Field Research and Development Plan of Guangdong Province, China","award":["D18019"],"award-info":[{"award-number":["D18019"]}]},{"name":"the 111 Project","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"the 111 Project","award":["2019B020221001"],"award-info":[{"award-number":["2019B020221001"]}]},{"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>Data processing of low-altitude remote sensing visible images from UAVs is one of the hot research topics in precision agriculture aviation. In order to solve the problems of large model size with slow detection speed that lead to the inability to process images in real time, this paper proposes a lightweight target detector CURI-YOLOv7 based on YOLOv7tiny which is suitable for individual citrus tree detection from UAV remote sensing imagery. This paper augmented the dataset with morphological changes and Mosica with Mixup. A backbone based on depthwise separable convolution and the MobileOne-block module was designed to replace the backbone of YOLOv7tiny. SPPF (spatial pyramid pooling fast) was used to replace the original spatial pyramid pooling structure. Additionally, we redesigned the neck by adding GSConv and depth-separable convolution and deleted its input layer from the backbone with a size of (80, 80) and its output layer from the head with a size of (80, 80). A new ELAN structure was designed, and the redundant convolutional layers were deleted. The experimental results show that the GFLOPs = 1.976, the parameters = 1.018 M, the weights = 3.98 MB, and the mAP = 90.34% for CURI-YOLOv7 in the UAV remote sensing imagery of the citrus trees dataset. The detection speed of a single image is 128.83 on computer and 27.01 on embedded devices. Therefore, the CURI-YOLOv7 model can basically achieve the function of individual tree detection in UAV remote sensing imagery on embedded devices. This forms a foundation for the subsequent UAV real-time identification of the citrus tree with its geographic coordinates positioning, which is conducive to the study of precise agricultural management of citrus orchards.<\/jats:p>","DOI":"10.3390\/rs15194647","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T05:32:45Z","timestamp":1695360765000},"page":"4647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7795-9277","authenticated-orcid":false,"given":"Yali","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"}]},{"given":"Xipeng","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"}]},{"given":"Jun","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China"}]},{"given":"Linlin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China"}]},{"given":"Haoxin","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}]},{"given":"Kangting","family":"Yan","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, Wushan Road, Guangzhou 510642, China"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"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, Wushan Road, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Development status and countermeasures of agricultural aviation in China","volume":"33","author":"Zhou","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, L., Lan, Y., Zhang, Y., Zhang, H., Tahir, M.N., Ou, S., Liu, X., and Chen, P. (2019). Applications and prospects of agricultural unmanned aerial vehicle obstacle avoidance technology in China. Sensors, 19.","DOI":"10.3390\/s19030642"},{"key":"ref_3","first-page":"53","article-title":"Current status and future trends of agricultural aerial spraying technology in China","volume":"45","author":"Zhang","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_4","first-page":"247","article-title":"Status and prospect of agricultural remote sensing","volume":"46","author":"Shi","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_5","unstructured":"Nie, J., and Yang, B. (2020). Monitoring Method of Crop Growth on a Large Scale Basedon Remote Sensing Technology. Comput. Simul., 37."},{"key":"ref_6","first-page":"1","article-title":"Present situation development trend and countermeasures of citrus industry in China","volume":"8","author":"Shan","year":"2008","journal-title":"J. Chin. Inst. Food Sci. Technol."},{"key":"ref_7","first-page":"63","article-title":"Extraction of cotton seedling growth information using UAV visible light remote sensing images","volume":"36","author":"Dai","year":"2010","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_8","first-page":"16","article-title":"Low altitude unmanned aerial vehicle remote sensing image processing based on visible band","volume":"37","author":"Deng","year":"2016","journal-title":"J. South China Agric. Univ."},{"key":"ref_9","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_10","first-page":"1190","article-title":"Prescription map generation intelligent system of precision agriculture based on knowledge model and WebGIS","volume":"pp","author":"Chen","year":"2007","journal-title":"Sci. Agric. Sin."},{"key":"ref_11","first-page":"72","article-title":"Research status and prospect of cotton terminal bud identification and location technology","volume":"39","author":"Hao","year":"2018","journal-title":"J. Chin. Agric. Mech."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tian, H., Fang, X., Lan, Y., Ma, C., Huang, H., Lu, X., Zhao, D., Liu, H., and Zhang, Y. (2022). Extraction of Citrus Trees from UAV Remote Sensing Imagery Using YOLOv5s and Coordinate Transformation. Remote Sens., 14.","DOI":"10.3390\/rs14174208"},{"key":"ref_13","first-page":"68","article-title":"Extraction of citrus crown parameters using UAV platform","volume":"37","author":"Shu","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_14","first-page":"133","article-title":"Monitoring method for UAV image of greenhouse and plastic-mulched Landcover based on deep learning","volume":"49","author":"Sun","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_15","first-page":"53","article-title":"UAV images for detecting maize tassel based on YOLO_X and transfer learning","volume":"38","author":"Wang","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_16","first-page":"155","article-title":"Wheat ear counting method in UAV images based on TPH-YOLO","volume":"39","author":"Bao","year":"2023","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Lu, X., Li, W., Yan, K., Mo, Z., Lan, Y., and Wang, L. (2023). Detection of Power Poles in Orchards Based on Improved Yolov5s Model. Agronomy, 13.","DOI":"10.3390\/agronomy13071705"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Luo, X., Wu, Y., and Zhao, L. (2022). YOLOD: A Target Detection Method for UAV Aerial Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14143240"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhou, J., Yang, Y., Liu, L., Liu, F., and Kong, W. (2022). Rapid Target Detection of Fruit Trees Using UAV Imaging and Improved Light YOLOv4 Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14174324"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Basso, M., Stocchero, D., Ventura Bayan Henriques, R., Vian, A.L., Bredemeier, C., Konzen, A.A., and Pignaton de Freitas, E. (2019). Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UA V-Based Agrochemical Spraying. Sensors, 19.","DOI":"10.3390\/s19245397"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ki, M., Cha, J., and Lyu, H. (2018, January 17\u201319). Detect and Avoid System Based on Multi Sensor Fusion for UAV. Proceedings of the 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICTC.2018.8539587"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., and Miao, Y. (2021). Review of image classification algorithms based on convolutional neural networks. Remote Sens., 13.","DOI":"10.3390\/rs13224712"},{"key":"ref_23","first-page":"229","article-title":"Fast recognition method for tomatoes under complex environments based on improved YOLO","volume":"51","author":"Liu","year":"2020","journal-title":"Trans. CSAM"},{"key":"ref_24","unstructured":"(2023, June 15). labelImg. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vasu PK, A., Gabriel, J., Zhu, J., Tuzel, O., and Ranjan, A. (2022). An improved one millisecond mobile backbone. arXiv.","DOI":"10.1109\/CVPR52729.2023.00764"},{"key":"ref_26","unstructured":"(2023, July 07). yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, J., and Ye, J. (2023). Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices. Appl. Sci., 13.","DOI":"10.3390\/app13074402"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_29","first-page":"191","article-title":"Recognizing apple targets before thinning using improved YOLOv7","volume":"39","author":"Long","year":"2023","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_30","first-page":"27","article-title":"Small target detection algorithm for aerial photography based on residual network optimization","volume":"41","author":"Li","year":"2022","journal-title":"Foreign Electron. Meas. Technol."},{"key":"ref_31","unstructured":"Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., and Ren, Q. (2022). Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles. arXiv."},{"key":"ref_32","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, M., Gao, F., Yang, W., and Zhang, H. (2023). Wildlife Object Detection Method Applying Segmentation Gradient Flow and Fea ture Dimensionality Reduction. Electronics, 12.","DOI":"10.3390\/electronics12020377"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"703005","DOI":"10.3788\/IRLA201847.0703005","article-title":"A lightweight small object detection algorithm based on improved SSD","volume":"47","author":"Wu","year":"2018","journal-title":"Infrared. Laser Eng."},{"key":"ref_35","first-page":"181","article-title":"Design and implementation of lightweight network based on improved YOLOv4 algorithm","volume":"48","author":"Kong","year":"2022","journal-title":"Comput. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Caba, J., D\u00edaz, M., Barba, J., Guerra, R., de la Torre, J.A., and L\u00f3pez, S. (2020). Fpga-based on-board hyperspec tral imaging compression: Benchmarking performance and energy efficiency against gpu implementations. Remote Sens., 12.","DOI":"10.3390\/rs12223741"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, Q., Wu, H., Zhao, C., Teng, G., and Li, J. (2021). Low-Altitude Remote Sensing Opium Poppy Image Detection Basedon Modified YOLOv3. Remote Sens., 13.","DOI":"10.3390\/rs13112130"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2015","journal-title":"Ieee Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2015). SSD: Single Shot MultiBox Detector. arXiv.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_40","first-page":"253","article-title":"Method for detecting rice flowering spikelets using visible light images","volume":"37","author":"Zhang","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_41","first-page":"1","article-title":"Research progress of two-dimensional human pose estimation based on deep learning","volume":"47","author":"Liu","year":"2021","journal-title":"Comput. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:55:43Z","timestamp":1760129743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":41,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194647"],"URL":"https:\/\/doi.org\/10.3390\/rs15194647","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,22]]}}}