{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T16:23:32Z","timestamp":1785774212902,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Major Science and Technology Program of Inner Mongolia Autonomous Region","award":["2020ZD0004"],"award-info":[{"award-number":["2020ZD0004"]}]},{"name":"the Major Science and Technology Program of Inner Mongolia Autonomous Region","award":["2021YFD1300503"],"award-info":[{"award-number":["2021YFD1300503"]}]},{"name":"the Major Science and Technology Program of Inner Mongolia Autonomous Region","award":["2022BBF02021"],"award-info":[{"award-number":["2022BBF02021"]}]},{"name":"the Major Science and Technology Program of Inner Mongolia Autonomous Region","award":["CAAS-ASTIP-2016-AII"],"award-info":[{"award-number":["CAAS-ASTIP-2016-AII"]}]},{"name":"the National Key Research and Development Program of China","award":["2020ZD0004"],"award-info":[{"award-number":["2020ZD0004"]}]},{"name":"the National Key Research and Development Program of China","award":["2021YFD1300503"],"award-info":[{"award-number":["2021YFD1300503"]}]},{"name":"the National Key Research and Development Program of China","award":["2022BBF02021"],"award-info":[{"award-number":["2022BBF02021"]}]},{"name":"the National Key Research and Development Program of China","award":["CAAS-ASTIP-2016-AII"],"award-info":[{"award-number":["CAAS-ASTIP-2016-AII"]}]},{"name":"the Key Research and Development Program of Ningxia Autonomous Region","award":["2020ZD0004"],"award-info":[{"award-number":["2020ZD0004"]}]},{"name":"the Key Research and Development Program of Ningxia Autonomous Region","award":["2021YFD1300503"],"award-info":[{"award-number":["2021YFD1300503"]}]},{"name":"the Key Research and Development Program of Ningxia Autonomous Region","award":["2022BBF02021"],"award-info":[{"award-number":["2022BBF02021"]}]},{"name":"the Key Research and Development Program of Ningxia Autonomous Region","award":["CAAS-ASTIP-2016-AII"],"award-info":[{"award-number":["CAAS-ASTIP-2016-AII"]}]},{"name":"the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences","award":["2020ZD0004"],"award-info":[{"award-number":["2020ZD0004"]}]},{"name":"the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences","award":["2021YFD1300503"],"award-info":[{"award-number":["2021YFD1300503"]}]},{"name":"the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences","award":["2022BBF02021"],"award-info":[{"award-number":["2022BBF02021"]}]},{"name":"the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences","award":["CAAS-ASTIP-2016-AII"],"award-info":[{"award-number":["CAAS-ASTIP-2016-AII"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model\u2019s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model\u2019s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep\u2019s daily behaviour for precision livestock management, promoting modern husbandry development.<\/jats:p>","DOI":"10.3390\/s23104752","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T08:28:56Z","timestamp":1684139336000},"page":"4752","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Grazing Sheep Behaviour Recognition Based on Improved YOLOV5"],"prefix":"10.3390","volume":"23","author":[{"given":"Tianci","family":"Hu","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9400-1105","authenticated-orcid":false,"given":"Ruirui","family":"Yan","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengxiang","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nividita Varun","family":"Chand","sequence":"additional","affiliation":[{"name":"Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"College of Agriculture, Fisheries and Forestry, Fiji National University, Suva P.O. Box 7222, Fiji"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Agricultural Information Technology Research Centre, Urumqi 830052, China"},{"name":"Ministry of Education Engineering Research Centre for Intelligent Agriculture, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leifeng","family":"Guo","sequence":"additional","affiliation":[{"name":"Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwei","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Animal Sciences, Inner Mongolia Agricultural University, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.livsci.2017.05.014","article-title":"Implementation of machine vision for detecting behaviour of cattle and pigs","volume":"202","author":"Nasirahmadi","year":"2017","journal-title":"Livest. Sci."},{"key":"ref_2","unstructured":"Pedersen, L.J. (2018). Advances in Pig Welfare, Woodhead Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.tvjl.2016.09.005","article-title":"Early detection of health and welfare compromises through automated detection of behavioural changes in pigs","volume":"217","author":"Matthews","year":"2016","journal-title":"Vet. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.biosystemseng.2020.01.016","article-title":"An automatic recognition framework for sow daily behaviours based on motion and image analyses","volume":"192","author":"Yang","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105706","DOI":"10.1016\/j.compag.2020.105706","article-title":"Automatic behavior recognition of group-housed goats using deep learning","volume":"177","author":"Jiang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3893","DOI":"10.3168\/jds.2016-12055","article-title":"The use of infrared thermography and accelerometers for remote monitoring of dairy cow health and welfare","volume":"100","author":"Stewart","year":"2017","journal-title":"J. Dairy Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.livsci.2017.09.003","article-title":"Recognition and drinking behaviour analysis of individual pigs based on machine vision","volume":"205","author":"Zhu","year":"2017","journal-title":"Livest. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compag.2017.02.019","article-title":"Development of an early detection system for lameness of broilers using computer vision","volume":"136","author":"Aydin","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compag.2019.01.025","article-title":"Lameness detection of dairy cows based on a double normal background statistical model","volume":"158","author":"Jiang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.biosystemseng.2019.11.017","article-title":"Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector","volume":"189","author":"Wu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.biosystemseng.2019.11.013","article-title":"Automatic recognition of lactating sow postures by refined two-stream RGB-D faster R-CNN","volume":"189","author":"Zhu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1017\/S1751731116001208","article-title":"A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method","volume":"11","author":"Nasirahmadi","year":"2017","journal-title":"Animal"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compag.2015.10.023","article-title":"Using machine vision for investigation of changes in pig group lying patterns","volume":"119","author":"Nasirahmadi","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2020.05.010","article-title":"Classification of drinking and drinker-playing in pigs by a video-based deep learning method","volume":"196","author":"Chen","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104982","DOI":"10.1016\/j.compag.2019.104982","article-title":"FLYOLOv3 deep learning for key parts of dairy cow body detection","volume":"166","author":"Jiang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107010","DOI":"10.1016\/j.compag.2022.107010","article-title":"Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect","volume":"198","author":"Cheng","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1109\/TII.2018.2875149","article-title":"Automatic Fruit Classification Using Deep Learning for Industrial Applications","volume":"15","author":"Hossain","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and Understanding Convolutional Networks. Proceedings of the Computer Vision\u2013ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.compag.2018.11.002","article-title":"Feeding behavior recognition for group-housed pigs with the Faster R-CNN","volume":"155","author":"Yang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.compag.2018.09.030","article-title":"Dairy goat detection based on Faster R-CNN from surveillance video","volume":"154","author":"Wang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/01431161.2019.1624858","article-title":"Cattle detection and counting in UAV images based on convolutional neural networks","volume":"41","author":"Shao","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107579","DOI":"10.1016\/j.compag.2022.107579","article-title":"Cattle body detection based on YOLOv5-ASFF for precision livestock farming","volume":"204","author":"Qiao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, X., Yan, J., Qiu, X., Yao, X., Tian, Y., Zhu, Y., and Cao, W. (2021). A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13163095"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111808","DOI":"10.1016\/j.postharvbio.2021.111808","article-title":"Apple stem\/calyx real-time recognition using YOLO-v5 algorithm for fruit automatic loading system","volume":"185","author":"Wang","year":"2022","journal-title":"Postharvest Biol. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"012129","DOI":"10.1088\/1742-6596\/1651\/1\/012129","article-title":"Research on Sheep Recognition Algorithm Based on Deep Learning in Animal Husbandry","volume":"1651","author":"Ma","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_28","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection 2020. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. arXiv.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201320). Selective Kernel Networks. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106780","DOI":"10.1016\/j.compag.2022.106780","article-title":"An Improved YOLOv5 Model Based on Visual Attention Mechanism: Application to Recognition of Tomato Virus Disease","volume":"194","author":"Qi","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107194","DOI":"10.1016\/j.compag.2022.107194","article-title":"A Deep Learning Approach Incorporating YOLO v5 and Attention Mechanisms for Field Real-Time Detection of the Invasive Weed Solanum Rostratum Dunal Seedlings","volume":"199","author":"Wang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, R., Gao, Z., Li, Q., Zhao, C., Gao, R., Zhang, H., Li, S., and Feng, L. (2022). Detection Method of Cow Estrus Behavior in Natural Scenes Based on Improved YOLOv5. Agriculture, 12.","DOI":"10.3390\/agriculture12091339"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4752\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:34:58Z","timestamp":1760124898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,15]]},"references-count":35,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104752"],"URL":"https:\/\/doi.org\/10.3390\/s23104752","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,15]]}}}