{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:33:01Z","timestamp":1776443581891,"version":"3.51.2"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T00:00:00Z","timestamp":1678838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDA26010201"],"award-info":[{"award-number":["XDA26010201"]}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["2021ZD0044"],"award-info":[{"award-number":["2021ZD0044"]}]},{"name":"Science and Technology Major Project of Inner Mongolia Autonomous Region of China","award":["XDA26010201"],"award-info":[{"award-number":["XDA26010201"]}]},{"name":"Science and Technology Major Project of Inner Mongolia Autonomous Region of China","award":["2021ZD0044"],"award-info":[{"award-number":["2021ZD0044"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The mAP of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects.<\/jats:p>","DOI":"10.3390\/rs15061593","type":"journal-article","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T04:39:45Z","timestamp":1678855185000},"page":"1593","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Lightweight and High-Accuracy Deep Learning Method for Grassland Grazing Livestock Detection Using UAV Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuhang","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Lingling","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1377-8394","authenticated-orcid":false,"given":"Dongliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Surface Pattern and Simulation, Institute of Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Xinhong","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Qingchuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Inner Mongolia North Heavy Industries Group Co., Ltd., Baotou 014033, China"}]},{"given":"Xiaoxin","family":"Hou","sequence":"additional","affiliation":[{"name":"Inner Mongolia North Heavy Industries Group Co., Ltd., Baotou 014033, China"}]},{"given":"Guangzhou","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,15]]},"reference":[{"key":"ref_1","first-page":"4099","article-title":"Grassland Livestock Real-Time Detection and Weight Estimation Based on Unmanned Aircraft System Video Streams","volume":"40","author":"Wang","year":"2021","journal-title":"Chin. 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