{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T20:59:45Z","timestamp":1789678785954,"version":"4.0.1"},"reference-count":40,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T00:00:00Z","timestamp":1744502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"key R&amp;D program of Shanxi Province","award":["202302010101002"],"award-info":[{"award-number":["202302010101002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>In modern large-scale pig farming, accurately identifying sow estrus and ensuring timely breeding are crucial for maximizing economic benefits. However, the short duration of estrus and the reliance on subjective human judgment pose significant challenges for precise insemination timing. To enable non-contact, automated estrus detection, this study proposes an improved algorithm, Enhanced Context-Attention YOLO (ECA-YOLO), based on YOLOv11. The model utilizes ocular appearance features\u2014eye\u2019s spirit, color, shape, and morphology\u2014across different estrus stages as key indicators. The MSCA module enhances small-object detection efficiency, while the PPA and GAM modules improve feature extraction capabilities. Additionally, the Adaptive Threshold Focal Loss (ATFL) function increases the model\u2019s sensitivity to hard-to-classify samples, enabling accurate estrus stage classification. The model was trained and validated on a dataset comprising 4461 images of sow eyes during estrus and was benchmarked against YOLOv5n, YOLOv7tiny, YOLOv8n, YOLOv10n, YOLOv11n, and Faster R-CNN. Experimental results demonstrate that ECA-YOLO achieves a mean average precision (mAP) of 93.2%, an F1-score of 88.0%, with 5.31M parameters, and FPS reaches 75.53 frames per second, exhibiting superior overall performance. The findings confirm the feasibility of using ocular features for estrus detection and highlight the potential of ECA-YOLO for real-time, accurate monitoring of sow estrus under complex farming conditions. This study lays the groundwork for automated estrus detection in intensive pig farming.<\/jats:p>","DOI":"10.3390\/ani15081127","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T03:39:16Z","timestamp":1744601956000},"page":"1127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Lightweight Model for Small-Target Pig Eye Detection in Automated Estrus Recognition"],"prefix":"10.3390","volume":"15","author":[{"given":"Min","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Animal Science, Shanxi Agricultural University, Taigu 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongpeng","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Animal Science, Shanxi Agricultural University, Taigu 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Animal Science, Shanxi Agricultural University, Taigu 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangying","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Animal Science, Shanxi Agricultural University, Taigu 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riliang","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Animal Science, Shanxi Agricultural University, Taigu 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Agricultural Engineering, Shanxi Agricultural University, Taigu 030801, China"},{"name":"Dryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Taigu 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40813-023-00303-3","article-title":"The evaluation of an artificial intelligence system for estrus detection in sows","volume":"9","author":"Verhoeven","year":"2023","journal-title":"Porc. Health Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1530\/jrf.0.1040099","article-title":"Effects of time of insemination relative to ovulation, as determined by ultrasonography, on fertilization rate and accessory sperm count in sows","volume":"104","author":"Soede","year":"1995","journal-title":"Reproduction"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"944","DOI":"10.2527\/1996.745944x","article-title":"Relationship of weaning-to-estrus interval to timing of ovulation and fertilization in sows","volume":"74","author":"Kemp","year":"1996","journal-title":"J. Anim. Sci."},{"key":"ref_4","first-page":"2148","article-title":"M\u00fcller and macrophage-like cell interactions in an organotypic culture of porcine neuroretina","volume":"14","author":"Pastor","year":"2008","journal-title":"Mol. Vis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109975","DOI":"10.1016\/j.compag.2025.109975","article-title":"Research on improved sound recognition model for oestrus detection in sows","volume":"231","author":"Cao","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100090","DOI":"10.1016\/j.atech.2022.100090","article-title":"Detecting sow vulva size change around estrus using machine vision technology","volume":"3","author":"Xu","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_7","unstructured":"McGlone, J.J., Aviles-Rosa, E.O., Archer, C., Wilson, M.M., Jones, K.D., Matthews, E.M., Gonzalez, A.A., and Reyes, E. (2020). Understanding sow sexual behavior and the application of the boar pheromone to stimulate sow reproduction. Animal Reproduction in Veterinary Medicine, IntechOpen."},{"key":"ref_8","first-page":"654","article-title":"Behavioural pattern of oestrus in the sow","volume":"60","author":"Nath","year":"1983","journal-title":"Indian Vet. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kraeling, R.R., and Webel, S.K. (2015). Current strategies for reproductive management of gilts and sows in North America. J. Anim. Sci. Biotechnol., 6.","DOI":"10.1186\/2049-1891-6-3"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1017\/S1751731117001975","article-title":"Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle","volume":"12","author":"Reith","year":"2018","journal-title":"Animal"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lei, K., Zong, C., Du, X., Teng, G., and Feng, F. (2021). Oestrus analysis of sows based on bionic boars and machine vision technology. Animals, 11.","DOI":"10.3390\/ani11061485"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104552","DOI":"10.1016\/j.psj.2024.104552","article-title":"Research on machine vision online monitoring system for egg production and quality in cage environment","volume":"104","author":"Wu","year":"2025","journal-title":"Poult. Sci."},{"key":"ref_13","first-page":"52","article-title":"PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets","volume":"15","author":"Xia","year":"2025","journal-title":"Artif. Intell. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100785","DOI":"10.1016\/j.atech.2025.100785","article-title":"PBR-YOLO: A lightweight piglet multi-behavior recognition algorithm based on improved yolov8","volume":"10","author":"Luo","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, M., Li, L., Hu, H., Liu, Y., Yao, Y., and Song, R. (2024, January 22\u201324). IAT-YOLO: A black pig detection model for use in low-light environments. Proceedings of the 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Zhuhai, China.","DOI":"10.1109\/ICSIDP62679.2024.10869173"},{"key":"ref_16","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_17","first-page":"194","article-title":"Automatic detection of sow estrus using a lightweight real-time detector and thermal images","volume":"16","author":"Zheng","year":"2023","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101863","DOI":"10.1016\/j.ecoinf.2022.101863","article-title":"A lightweight CNN-based model for early warning in sow oestrus sound monitoring","volume":"72","author":"Wang","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"012049","DOI":"10.1088\/1742-6596\/2203\/1\/012049","article-title":"A Fusion Feature for the Oestrous Sow Sound Identification Based on Convolutional Neural Networks","volume":"2203","author":"Chen","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107949","DOI":"10.1016\/j.compag.2023.107949","article-title":"Discrimination strategy using machine learning technique for oestrus detection in dairy cows by a dual-channel-based acoustic tag","volume":"210","author":"Wang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.dcmed.2024.09.001","article-title":"The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine","volume":"7","author":"Gao","year":"2024","journal-title":"Digit. Chin. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"237","DOI":"10.12677\/MD.2022.123038","article-title":"Exploring the Innovation of Eye Diagnosis Theory Based on the Principle of State Identification","volume":"12","author":"Zou","year":"2022","journal-title":"Med. Diagn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17905","DOI":"10.1109\/ACCESS.2018.2812208","article-title":"Eye recognition with mixed convolutional and residual network (MiCoRe-Net)","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1167\/iovs.06-0352","article-title":"Changes in spherical aberration after lens refilling with a silicone oil","volume":"48","author":"Wong","year":"2007","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.exer.2005.03.014","article-title":"The pig eye as a novel model of glaucoma","volume":"81","author":"Urcola","year":"2005","journal-title":"Exp. Eye Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3346\/jkms.2009.24.2.189","article-title":"DNA microarray-based gene expression profiling in porcine keratocytes and corneal endothelial cells and comparative analysis associated with Xeno-related rejection","volume":"24","author":"Kim","year":"2009","journal-title":"J. Korean Med. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.biosystemseng.2024.05.016","article-title":"Blood vitamin A level prediction in Japanese black cattle based on chromatic and dynamic eye features using double imaging system","volume":"244","author":"Li","year":"2024","journal-title":"Biosyst. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.2527\/jas.2010-3197","article-title":"Retinal image recognition for verifying the identity of fattening and replacement lambs","volume":"89","author":"Caja","year":"2011","journal-title":"J. Anim. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108963","DOI":"10.1016\/j.compag.2024.108963","article-title":"CattNIS: Novel identification system of cattle with retinal images based on feature matching method","volume":"221","author":"Cihan","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108549","DOI":"10.1016\/j.compag.2023.108549","article-title":"Pig eye area temperature extraction algorithm based on registered images","volume":"217","author":"Zhang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1016\/j.visres.2010.03.009","article-title":"Morphological differences between the eyeballs of nocturnal and diurnal amniotes revisited from optical perspectives of visual environments","volume":"50","author":"Schmitz","year":"2010","journal-title":"Vis. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1016\/j.theriogenology.2014.07.017","article-title":"Variations in the vulvar temperature of sows during proestrus and estrus as determined by infrared thermography and its relation to ovulation","volume":"82","author":"Lyazrhi","year":"2014","journal-title":"Theriogenology"},{"key":"ref_33","unstructured":"Adrian, T.E. (1997). Radioimmunoassay. Neuropeptide Protocols, Humana Press Inc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"116087","DOI":"10.1016\/j.eswa.2021.116087","article-title":"Structural similarity index (SSIM) revisited: A data-driven approach","volume":"189","author":"Bakurov","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_35","first-page":"125","article-title":"A small target detection method based on visual attention","volume":"49","author":"Xie","year":"2013","journal-title":"Comput. Eng. Appl."},{"key":"ref_36","first-page":"1140","article-title":"Segnext: Rethinking convolutional attention design for semantic segmentation","volume":"35","author":"Guo","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, S., Zheng, S., Xu, W., Xu, R., Wang, C., Zhang, J., Teng, X., Li, A., and Guo, L. (2024, January 15\u201319). Hcf-net: Hierarchical context fusion network for infrared small object detection. Proceedings of the 2024 IEEE International Conference on Multimedia and Expo (ICME), Niagara Falls, ON, Canada.","DOI":"10.1109\/ICME57554.2024.10687431"},{"key":"ref_38","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tang, F., Ding, J., Quan, Q., Wang, L., Ning, C., and Zhou, S.K. (2024, January 27\u201330). Cmunext: An efficient medical image segmentation network based on large kernel and skip fusion. Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece.","DOI":"10.1109\/ISBI56570.2024.10635609"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106131","DOI":"10.1016\/j.cor.2022.106131","article-title":"Comparing two SVM models through different metrics based on the confusion matrix","volume":"152","author":"Alcaraz","year":"2023","journal-title":"Comput. Oper. Res."}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/15\/8\/1127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:13:46Z","timestamp":1760030026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/15\/8\/1127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,13]]},"references-count":40,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["ani15081127"],"URL":"https:\/\/doi.org\/10.3390\/ani15081127","relation":{},"ISSN":["2076-2615"],"issn-type":[{"value":"2076-2615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,13]]}}}