{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T02:23:57Z","timestamp":1781144637009,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:00:00Z","timestamp":1650758400000},"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":["32072786"],"award-info":[{"award-number":["32072786"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The feeding behaviour of cows is an essential sign of their health in dairy farming. For the impression of cow health status, precise and quick assessment of cow feeding behaviour is critical. This research presents a method for monitoring dairy cow feeding behaviour utilizing edge computing and deep learning algorithms based on the characteristics of dairy cow feeding behaviour. Images of cow feeding behaviour were captured and processed in real time using an edge computing device. A DenseResNet-You Only Look Once (DRN-YOLO) deep learning method was presented to address the difficulties of existing cow feeding behaviour detection algorithms\u2019 low accuracy and sensitivity to the open farm environment. The deep learning and feature extraction enhancement of the model was improved by replacing the CSPDarknet backbone network with the self-designed DRNet backbone network based on the YOLOv4 algorithm using multiple feature scales and the Spatial Pyramid Pooling (SPP) structure to enrich the scale semantic feature interactions, finally achieving the recognition of cow feeding behaviour in the farm feeding environment. The experimental results showed that DRN-YOLO improved the accuracy, recall, and mAP by 1.70%, 1.82%, and 0.97%, respectively, compared to YOLOv4. The research results can effectively solve the problems of low recognition accuracy and insufficient feature extraction in the analysis of dairy cow feeding behaviour by traditional methods in complex breeding environments, and at the same time provide an important reference for the realization of intelligent animal husbandry and precision breeding.<\/jats:p>","DOI":"10.3390\/s22093271","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T22:22:41Z","timestamp":1650838961000},"page":"3271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6264-1183","authenticated-orcid":false,"given":"Zhenwei","family":"Yu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuehua","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai\u2019an 271018, China"},{"name":"Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, Tai\u2019an 271018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sufang","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Shangdong Agriculture University, Tai\u2019an 271018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixue","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanhua","family":"Song","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6417-9666","authenticated-orcid":false,"given":"Yinfa","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fade","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonghua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Animal Science and Technology, Shangdong Agriculture University, Tai\u2019an 271018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4103-2079","authenticated-orcid":false,"given":"Fuyang","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/S0301-6226(03)00040-X","article-title":"Effects of health disorders on feed intake and milk production in dairy cows","volume":"83","author":"Bareille","year":"2003","journal-title":"Livest. Prod. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"630","DOI":"10.3168\/jds.2011-4350","article-title":"Sickness behavior in dairy cows during Escherichia coli mastitis","volume":"95","author":"Fogsgaard","year":"2012","journal-title":"J. Dairy Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.3168\/jds.2014-8347","article-title":"Behavioral changes in freestall-housed dairy cows with naturally occurring clinical mastitis","volume":"98","author":"Fogsgaard","year":"2015","journal-title":"J. Dairy Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.3389\/fvets.2016.00037","article-title":"Lameness Affects Cow Feeding But Not Rumination Behavior as Characterized from Sensor Data","volume":"3","author":"Thorup","year":"2016","journal-title":"Front. Vet. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.biosystemseng.2020.07.019","article-title":"Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on Convolutional Neural Networks (CNN)","volume":"198","author":"Achour","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106255","DOI":"10.1016\/j.compag.2021.106255","article-title":"Behaviour recognition of pigs and cattle: Journey from computer vision to deep learning","volume":"187","author":"Chen","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.compag.2016.07.005","article-title":"Probabilities of cattle participating in eating and drinking behavior when located at feeding and watering locations by a real time location system","volume":"127","author":"Shane","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.compag.2018.07.005","article-title":"A hidden Markov model to estimate the time dairy cows spend in feeder based on indoor positioning data","volume":"152","author":"Pastell","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.compag.2014.08.001","article-title":"Localisation and identification performances of a real-time location system based on ultra wide band technology for monitoring and tracking dairy cow behaviour in a semi-open free-stall barn","volume":"108","author":"Porto","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.compag.2017.10.029","article-title":"Automatic individual identification of Holstein dairy cows using tailhead images","volume":"142","author":"Li","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, G., Xiong, Y., Du, Q., Shi, Z., and Gates, R.S. (2021). Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition. Sensors, 21.","DOI":"10.3390\/s21155231"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106495","DOI":"10.1016\/j.compag.2021.106495","article-title":"Automatic recognition method of cow ruminating behaviour based on edge computing","volume":"191","author":"Shen","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kang, X., Zhang, X.D., and Liu, G. (2021). A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications. Sensors, 21.","DOI":"10.3390\/s21030753"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109497","DOI":"10.1109\/ACCESS.2021.3099212","article-title":"Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information","volume":"9","author":"Tian","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.compag.2018.08.033","article-title":"Surface electromyography segmentation and feature extraction for ingestive behavior recognition in ruminants","volume":"153","author":"Campos","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, T., Pang, B., Ai, S., and Sun, X. (2020). Study on Visual Detection Algorithm of Sea Surface Targets Based on Improved YOLOv3. Sensors, 20.","DOI":"10.3390\/s20247263"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, A., Noguchi, R., and Ahamed, T. (2022). Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN. Sensors, 22.","DOI":"10.3390\/s22052065"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.biosystemseng.2015.02.012","article-title":"The automatic detection of dairy cow feeding and standing behaviours in free-stall barns by a computer vision-based system","volume":"133","author":"Porto","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105345","DOI":"10.1016\/j.compag.2020.105345","article-title":"Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms","volume":"172","author":"Bezen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.compag.2016.04.026","article-title":"Automatic recognition of lactating sow behaviors through depth image processing","volume":"125","author":"Lao","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"386","DOI":"10.3168\/jds.2014-8964","article-title":"Short communication: Measuring feed volume and weight by machine vision","volume":"99","author":"Shelley","year":"2016","journal-title":"J. Dairy Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1007\/s10489-020-02149-6","article-title":"Densely connected convolutional networks-based COVID-19 screening model","volume":"51","author":"Singh","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ins.2020.02.067","article-title":"DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection","volume":"522","author":"Huang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, 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 2017), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_28","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., and Shum, H.Y. (2022). DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:59:53Z","timestamp":1760137193000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,24]]},"references-count":28,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093271"],"URL":"https:\/\/doi.org\/10.3390\/s22093271","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,24]]}}}