{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T21:28:01Z","timestamp":1782595681603,"version":"3.54.5"},"reference-count":26,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"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":["62261041"],"award-info":[{"award-number":["62261041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the increase in the scale of breeding at modern pastures, the management of dairy cows has become much more challenging, and individual recognition is the key to the implementation of precision farming. Based on the need for low-cost and accurate herd management and for non-stressful and non-invasive individual recognition, we propose a vision-based automatic recognition method for dairy cow ear tags. Firstly, for the detection of cow ear tags, the lightweight Small-YOLOV5s is proposed, and then a differentiable binarization network (DBNet) combined with a convolutional recurrent neural network (CRNN) is used to achieve the recognition of the numbers on ear tags. The experimental results demonstrated notable improvements: Compared to those of YOLOV5s, Small-YOLOV5s enhanced recall by 1.5%, increased the mean average precision by 0.9%, reduced the number of model parameters by 5,447,802, and enhanced the average prediction speed for a single image by 0.5 ms. The final accuracy of the ear tag number recognition was an impressive 92.1%. Moreover, this study introduces two standardized experimental datasets specifically designed for the ear tag detection and recognition of dairy cows. These datasets will be made freely available to researchers in the global dairy cattle community with the intention of fostering intelligent advancements in the breeding industry.<\/jats:p>","DOI":"10.3390\/s24072194","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T06:33:16Z","timestamp":1711693996000},"page":"2194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Research on the Vision-Based Dairy Cow Ear Tag Recognition Method"],"prefix":"10.3390","volume":"24","author":[{"given":"Tianhong","family":"Gao","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daoerji","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijuan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangzhong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihao","family":"Song","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"ref_1","first-page":"232","article-title":"Review of the economic situation of China\u2019s dairy industry in 2021 and outlook for 2022","volume":"58","author":"Liu","year":"2022","journal-title":"Chin. J. Anim. Husb."},{"key":"ref_2","first-page":"0081","article-title":"Comprehensive technical analysis of precision dairy farming for cows","volume":"2022","author":"Zhang","year":"2022","journal-title":"Chin. Sci. Technol. Period. Database Agric. Sci."},{"key":"ref_3","first-page":"110","article-title":"Comparative analysis of dairy farming efficiency in different dairy production areas in china\u2014Based on survey data from 266 farms","volume":"41","author":"Liu","year":"2020","journal-title":"Inst. Agric. Inf. Chin. Acad. Agric. Sci."},{"key":"ref_4","unstructured":"Liao, M., Zhu, Z., Shi, B., Xia, G., Bai, X., and Yuille, A.L. (2020, January 19). Real-time scene text detection with differentiable binarization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","article-title":"An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition","volume":"39","author":"Shi","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","first-page":"62","article-title":"Research progress on methods and application of dairy cow identification","volume":"24","author":"Sun","year":"2019","journal-title":"J. China Agric. Univ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.compag.2016.03.014","article-title":"From classical methods to animal biometrics: A review on cattle identification and tracking","volume":"123","author":"Awad","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","unstructured":"Ebert, B., and Whittenburg, B. (2006). Identification of Beef Animals (Tech. Rep. YANR-0170), Auburn University."},{"key":"ref_9","first-page":"204","article-title":"The end of the identity crisis? Advances in biometric markers for animal identification","volume":"62","author":"Barron","year":"2009","journal-title":"Ir. Vet. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.compag.2011.08.010","article-title":"The role of RFID in agriculture: Applications, limitations, and challenges","volume":"79","author":"Lunadei","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compag.2017.01.021","article-title":"Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioral activities from accelerometer data","volume":"134","author":"Arcidiacono","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","first-page":"138","article-title":"A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions","volume":"6","author":"Hossain","year":"2022","journal-title":"Artif. Intell. Agric."},{"key":"ref_13","unstructured":"Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., and Clark, C.E. (2024, March 26). Individual Cattle Identification Using a Deep Learning-Based Framework. IFAC-PapersOnLine. Available online: https:\/\/api.semanticscholar.org\/CorpusID:213360366."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106030","DOI":"10.1016\/j.compag.2021.106030","article-title":"A computer vision approach based on deep learning for the detection of dairy cows in free stall barn","volume":"182","author":"Tassinari","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, S., Zuo, X., and Yang, R. (2021, January 10\u201315). Angus Cattle Recognition Using Deep Learning. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412073"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hao, W., Ren, C., Han, M., Zhang, L., Li, F., and Liu, Z. (2023). Cattle Body Detection Based on YOLOv5-EMA for Precision Livestock Farming. Animals, 13.","DOI":"10.3390\/ani13223535"},{"key":"ref_17","first-page":"586","article-title":"Automatic location and recognition of cow\u2019s collar ID based on machine learning","volume":"44","author":"Zhang","year":"2021","journal-title":"J. Nanjing Agric. Univ."},{"key":"ref_18","unstructured":"Ilestrand, M. (2024, March 26). Automatic Ear Tag Recognition on Dairy Cows in Real Barn Environment.Agricultural and Food Sciences, Engineering. Available online: https:\/\/api.semanticscholar.org\/CorpusID:102490549."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zin, T.T., Pwint, M.Z., Seint, P.T., Thant, S., Misawa, S., Sumi, K., and Yoshida, K. (2020). Automatic cow location tracking system using ear tag visual analysis. Sensors, 20.","DOI":"10.3390\/s20123564"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bastiaansen, J.W.M., Hulsegge, B., Schokker, D., Ellen, E.D., Klermans, G.G.J., Taghavirazavizadeh, M., and Kamphuis, C. (2022). Continuous Real-Time Cow Identification by Reading Ear Tags from Live-Stream Video. Front. Anim. Sci., 3.","DOI":"10.3389\/fanim.2022.846893"},{"key":"ref_21","unstructured":"Glenn, J. (2024, March 06). YOLOv5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, C., Liu, M., Tuzel, O., and Xiao, J. (2016, January 20\u201324). R-CNN for Small Object Detection. Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan.","DOI":"10.1007\/978-3-319-54193-8_14"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 18\u201322). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Salt Lake City, UT, USA.","DOI":"10.1007\/978-3-030-01234-2_1"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:20:42Z","timestamp":1760106042000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,29]]},"references-count":26,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072194"],"URL":"https:\/\/doi.org\/10.3390\/s24072194","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,29]]}}}