{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:11:49Z","timestamp":1791378709367,"version":"4.3.2"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Inner Mongolia Scientific and Technological Project","award":["2025KYPT0088"],"award-info":[{"award-number":["2025KYPT0088"]}]},{"name":"Inner Mongolia Scientific and Technological Project","award":["2023YFJM0002"],"award-info":[{"award-number":["2023YFJM0002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In modern smart dairy farms, precise feed management and accurate monitoring of dairy cows\u2019 feeding behavior are crucial for improving production efficiency and reducing feeding costs. However, in practical applications, complex environmental factors such as varying illumination, frequent occlusion, and dense multi-targets pose significant challenges to real-time visual perception. To address these issues, this paper proposes a lightweight multi-target detection model, BFDet-YOLO, for the joint detection of dairy cows\u2019 feeding behavior and feed density levels in pasture environments. Based on the YOLOv10 framework, the model incorporates four targeted improvements: (1) a bidirectional feature fusion network (BiFPN) to address the insufficient multi-scale feature interaction between dairy cows (large targets) and feed particles (small targets); (2) a lightweight downsampling module (Adown) to preserve fine-grained features of feed particles and reduce the risk of small target miss detection; (3) an attention-enhanced detection head (SEAM) to mitigate occlusion interference caused by cow stacking and feed accumulation; (4) an improved bounding box regression loss function (DIoU) to optimize the localization accuracy of non-overlapping small targets. Additionally, this paper constructs a pasture-specific dataset integrating dairy cows\u2019 feeding behavior and feed distribution information, which is annotated and expanded by combining public datasets with on-site monitoring data. Experimental results demonstrate that BFDet-YOLO outperforms the original YOLOv10 and other mainstream target recognition models in terms of detection accuracy and robustness while maintaining a significantly streamlined model scale. On the constructed dataset, the model achieves 95.7% mAP@0.5 and 70.7% mAP@0.5:0.95 with only 1.85 M parameters. These results validate the effectiveness and deployability of the proposed method, providing a reliable visual perception solution for intelligent feeding systems and smart pasture management.<\/jats:p>","DOI":"10.3390\/s26041273","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:38:39Z","timestamp":1771231119000},"page":"1273","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-Object Detection of Forage Density and Dairy Cow Feeding Behavior Based on an Improved YOLOv10 Model for Smart Pasture Applications"],"prefix":"10.3390","volume":"26","author":[{"given":"Zhiwei","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China"},{"name":"Inner Mongolia Key Laboratory of Intelligent Perception and System Engineering, Hohhot 010080, China"},{"name":"Inner Mongolia Synergy Innovation Center of Perception Technology in Intelligent Agriculture and Animal Husbandry, Hohhot 010080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2920-9276","authenticated-orcid":false,"given":"Jiandong","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China"},{"name":"Inner Mongolia Key Laboratory of Intelligent Perception and System Engineering, Hohhot 010080, China"},{"name":"Inner Mongolia Synergy Innovation Center of Perception Technology in Intelligent Agriculture and Animal Husbandry, Hohhot 010080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Inner Mongolia Key Laboratory of Intelligent Perception and System Engineering, Hohhot 010080, China"},{"name":"Inner Mongolia Synergy Innovation Center of Perception Technology in Intelligent Agriculture and Animal Husbandry, Hohhot 010080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,15]]},"reference":[{"key":"ref_1","first-page":"111","article-title":"Experimental Study on Intelligent Pushing Method of Feeding Assistant Robot","volume":"50","author":"Zhang","year":"2022","journal-title":"J. South China Univ. Technol. Nat. Sci. Ed."},{"key":"ref_2","first-page":"78","article-title":"Design and Experiment of Intelligent Feed-pushing Robot Based on Information Fusion","volume":"54","author":"Zhang","year":"2023","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s12541-022-00742-y","article-title":"Autonomous System of TMR (Total Mixed Ration) Feed Feeding Robot for Smart Cattle Farm","volume":"24","author":"Bae","year":"2023","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_4","first-page":"293","article-title":"Autonomous Navigation System for Pasture Intelligent Overthrowing Grass Robot Based on Laser SLAM","volume":"54","author":"Song","year":"2023","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Guarnido-Lopez, P., Ramirez-Agudelo, J.F., Denimal, E., and Benaouda, M. (2024). Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m. Animals, 14.","DOI":"10.3390\/ani14192821"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"016048","DOI":"10.1088\/1361-6501\/ad9f8b","article-title":"Detection of group-housed pigs feeding behavior using deep learning and edge devices","volume":"36","author":"Gong","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"ref_7","first-page":"316","article-title":"Daily Behavior Recognition and Real-time Statistics System of Free-range Laying Hens Based on SEEC\u2013YOLO v5s","volume":"54","author":"Yang","year":"2023","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e2427","DOI":"10.7717\/peerj-cs.2427","article-title":"A real-time feeding behavior monitoring system for individual yak based on facial recognition model","volume":"10","author":"Yang","year":"2024","journal-title":"Peerj Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yu, Z., Liu, Y., Yu, S., Wang, R., Song, Z., Yan, Y., Li, Y., Wang, Z., and Tian, F. (2022). Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing. Sensors, 22.","DOI":"10.3390\/s22093271"},{"key":"ref_10","first-page":"782","article-title":"Detection of dairy cow feeding behavior based on video and BCE-YOLO model","volume":"45","author":"Zhang","year":"2024","journal-title":"J. South China Agric. Univ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.biosystemseng.2024.07.017","article-title":"IATEFF-YOLO: Focus on cow mounting detection during nighttime","volume":"246","author":"Dai","year":"2024","journal-title":"Biosyst. Eng."},{"key":"ref_12","first-page":"141","article-title":"Estrus Behavior Recognition of Dairy Cows Based on Improved YOLO v3 Model","volume":"52","author":"Wang","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.biosystemseng.2025.02.005","article-title":"Detection and tracking of oestrus dairy cows based on improved YOLOv8n and TransT models","volume":"252","author":"Wang","year":"2025","journal-title":"Biosyst. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107857","DOI":"10.1016\/j.compag.2023.107857","article-title":"YOLO-BYTE: An efficient multi-object tracking algorithm for automatic monitoring of dairy cows","volume":"209","author":"Zheng","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102304","DOI":"10.1016\/j.aquaeng.2022.102304","article-title":"Detection of residual feed in aquaculture using YOLO and Mask RCNN","volume":"100","author":"Hu","year":"2023","journal-title":"Aquac. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"742700","DOI":"10.1016\/j.aquaculture.2025.742700","article-title":"YOLO-feed: An advanced lightweight network enabling real-time, high-precision detection of feed pellets on CPU devices and its applications in quantifying individual fish feed intake","volume":"608","author":"Feng","year":"2025","journal-title":"Aquaculture"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yao, Q., Zheng, X., Zhou, G., and Zhang, J. (2024). SGR-YOLO: A method for detecting seed germination rate in wild rice. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1305081"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105057","DOI":"10.1016\/j.imavis.2024.105057","article-title":"ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation","volume":"147","author":"Kang","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4019","DOI":"10.1007\/s11760-024-03010-w","article-title":"Using improved YOLO V5s to recognize tomatoes in a continuous working environment","volume":"18","author":"Gao","year":"2024","journal-title":"Signal Image Video Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, J., Peng, Y., and Wang, Y. (2025). FreqDyn-YOLO: A High-Performance Multi-Scale Feature Fusion Algorithm for Detecting Plastic Film Residues in Farmland. Sensors, 25.","DOI":"10.3390\/s25164888"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yan, X., Sun, S., Zhu, H., Hu, Q., Ying, W., and Li, Y. (2025). DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images. Remote Sens., 17.","DOI":"10.20944\/preprints202506.0837.v1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8197","DOI":"10.1109\/TCSVT.2025.3544741","article-title":"Learning Discriminative Representation for Fine-Grained Object Detection in Remote Sensing Images","volume":"35","author":"Xie","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"109090","DOI":"10.1016\/j.compag.2024.109090","article-title":"Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review","volume":"223","author":"Badgujar","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","unstructured":"Jiang, P., Ergu, D., Liu, F., Cai, Y., and Ma, B. (2021, January 9\u201311). A Review of Yolo Algorithm Developments. Proceedings of the 8th International Conference on Information Technology and Quantitative Management (ITQM)\u2013Developing Global Digital Economy After COVID-19, Chengdu, China."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1123515","DOI":"10.1109\/ACCESS.2025.3586673","article-title":"A Benchmark Review of YOLO Algorithm Developments for Object Detection","volume":"13","author":"Hua","year":"2025","journal-title":"Ieee Access"},{"key":"ref_26","first-page":"89","article-title":"Cot-DCN-YOLO: Self-attention-enhancing YOLOv8s for detecting garbage bins in urban street view images","volume":"28","author":"Dong","year":"2025","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_27","first-page":"4","article-title":"YOLO deep learning algorithm for object detection in agriculture: A review","volume":"55","author":"Ramalingam","year":"2024","journal-title":"J. Agric. Eng."},{"key":"ref_28","unstructured":"Alif, M.A.R., and Hussain, M. (2024). YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain. arXiv."},{"key":"ref_29","first-page":"107984","article-title":"YOLOv10: Real-Time End-to-End Object Detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, A., Wang, C., Ji, T., Wang, Q., and Zhang, T. (2024). D3-YOLOv10: Improved YOLOv10-Based Lightweight Tomato Detection Algorithm Under Facility Scenario. Agriculture, 14.","DOI":"10.3390\/agriculture14122268"},{"key":"ref_31","first-page":"108","article-title":"Lightweight Daylily Grading and Detection Model Based on Improved YOLOv10","volume":"6","author":"Jin","year":"2024","journal-title":"Smart Agric."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1273\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T05:10:53Z","timestamp":1771996253000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1273"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,15]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26041273"],"URL":"https:\/\/doi.org\/10.3390\/s26041273","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,15]]}}}