{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T05:23:47Z","timestamp":1787117027132,"version":"build-2736575974"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Jiangsu agricultural science and technology innovation fund","award":["CX(23)1004"],"award-info":[{"award-number":["CX(23)1004"]}]},{"name":"CARS","award":["CARS-41"],"award-info":[{"award-number":["CARS-41"]}]},{"name":"Changzhou Modern Agriculture Science and Technology Innovation Center","award":["CAIC(2023)001"],"award-info":[{"award-number":["CAIC(2023)001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>In commercial floor-rearing environments for yellow-feathered broilers, visual screening of broilers with observable health-related abnormalities is challenged by low illumination, individual occlusion, and the limited use of local comb information. This study proposes a two-stage global\u2013local framework for yellow-feathered broilers. Images were collected from one commercial floor-rearing farm; after screening, 1463 images were used for key-region localization, and 2031 samples were constructed for health-status recognition. Samples were labeled as Healthy or Unhealthy by expert consensus according to observable phenotypic characteristics, rather than veterinary-confirmed disease diagnoses. First, a Dual-Target YOLO (DT-YOLO) model localized whole-body and comb regions. Second, a Transformer network integrated features from paired or single available inputs for classification. DT-YOLO achieved a mean Average Precision (mAP) of 97.56% at the 0.5 IoU threshold. For paired whole-body\u2013comb inputs, the proposed classification model achieved an Accuracy of 97.11%, Macro-Precision of 97.38%, Macro-Recall of 96.58%, and Macro-F1 of 96.95%. Under the mixed-input condition, the model maintained an Accuracy of 96.06% and a Macro-F1 of 96.02%. These findings indicate that combining whole-body and comb information can support visual screening of unhealthy broilers under the single-farm floor-rearing conditions represented in the current dataset. Its applicability to other farms, breeds, and management conditions requires independent external validation.<\/jats:p>","DOI":"10.3390\/ani16162559","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T10:04:36Z","timestamp":1786961076000},"page":"2559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Health Status Recognition of Yellow-Feathered Broilers in Floor-Rearing Environments via Whole-Body and Comb Feature Fusion"],"prefix":"10.3390","volume":"16","author":[{"given":"Jialei","family":"Xue","sequence":"first","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5117-5480","authenticated-orcid":false,"given":"Tian","family":"Hua","sequence":"additional","affiliation":[{"name":"College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongliang","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Bai","sequence":"additional","affiliation":[{"name":"Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5712-626X","authenticated-orcid":false,"given":"Guobin","family":"Chang","sequence":"additional","affiliation":[{"name":"College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0898-7864","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","first-page":"373","article-title":"Global Development Trends and Future Prospects of the Broiler Industry in 2025","volume":"62","author":"Zhang","year":"2026","journal-title":"Chin. J. Anim. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, Z., Willems, S., Liu, D., and Norton, T. (2025). How AI Improves Sustainable Chicken Farming: A Literature Review of Welfare, Economic, and Environmental Dimensions. Agriculture, 15.","DOI":"10.3390\/agriculture15192028"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104249","DOI":"10.1016\/j.psj.2024.104249","article-title":"Molecular and pathological screening of the current circulation of fowlpox and pigeon pox virus in backyard birds","volume":"103","author":"Mohamed","year":"2024","journal-title":"Poult. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qin, W., Yang, X., Yu, L., Chen, Y., Wang, J., Zhou, Y., and Zheng, W. (2025). Automated non-laying hen identification via deep learning: A phenotypic characteristics attribution analysis framework. Smart Agric. Technol., 101184.","DOI":"10.1016\/j.atech.2025.101184"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1017\/S0043933912000542","article-title":"Comb: An important reliable visual ornamental trait for selection in chickens","volume":"68","author":"Mukhtar","year":"2012","journal-title":"World\u2019s Poult. Sci. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"110492","DOI":"10.1016\/j.compag.2025.110492","article-title":"DCDNet: A deep neural network for dead chicken detection in layer farms","volume":"237","author":"Wu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hua, T., Fan, Q., Chen, R., Bi, Y., Bai, H., Wang, Z., Chang, G., and Zhao, W. (2026). Dual-Modal Chicken Mortality Detection Using Dynamic Hybrid Convolution-Based Feature Fusion. Animals, 16.","DOI":"10.3390\/ani16071057"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104724","DOI":"10.1016\/j.psj.2024.104724","article-title":"Top-view characterization of broiler walking ability and leg health using computer vision","volume":"104","author":"Fodor","year":"2025","journal-title":"Poult. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fang, C., Wu, Z., Zheng, H., Yang, J., Ma, C., and Zhang, T. (2024). MCP: Multi-chicken pose estimation based on transfer learning. Animals, 14.","DOI":"10.3390\/ani14121774"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100786","DOI":"10.1016\/j.atech.2025.100786","article-title":"Smart glasses in the chicken barn: Enhancing animal welfare through mixed reality","volume":"10","author":"Baltzer","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"110553","DOI":"10.1016\/j.compag.2025.110553","article-title":"Integration and evaluation of a low-cost intelligent system and its parameters for monitoring three-dimensional features of broiler chickens","volume":"237","author":"Asali","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"110541","DOI":"10.1016\/j.compag.2025.110541","article-title":"Cough sound recognition in poultry using portable microphones for precision medication guidance","volume":"237","author":"Zhou","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100856","DOI":"10.1016\/j.atech.2025.100856","article-title":"Optimizing poultry disease classification: A feature-based transfer learning approach","volume":"10","author":"Luo","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101307","DOI":"10.1016\/j.atech.2025.101307","article-title":"Advancements in artificial intelligence and machine learning for poultry farming: Applications, challenges, and future prospects","volume":"12","author":"Natsir","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107131","DOI":"10.1016\/j.compag.2022.107131","article-title":"Information perception in modern poultry farming: A review","volume":"199","author":"Wu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"106931","DOI":"10.1016\/j.compag.2022.106931","article-title":"Pose estimation-based lameness recognition in broiler using CNN-LSTM network","volume":"197","author":"Nasiri","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.compag.2017.11.032","article-title":"Development of an early warning algorithm to detect sick broilers","volume":"144","author":"Zhuang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.biosystemseng.2019.01.003","article-title":"Detection of sick broilers by digital image processing and deep learning","volume":"179","author":"Zhuang","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100324","DOI":"10.1016\/j.atech.2023.100324","article-title":"Research on broiler health status recognition method based on improved YOLOv5","volume":"6","author":"Cui","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110674","DOI":"10.1016\/j.compag.2025.110674","article-title":"Advancing animal farming with deep learning: A systematic review","volume":"237","author":"Rahman","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bakar, M.A.A.A., Ker, P.J., Tang, S.G.H., Baharuddin, M.Z., Lee, H.J., and Omar, A.R. (2023). Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models. Front. Vet. Sci., 10.","DOI":"10.3389\/fvets.2023.1174700"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ansarimovahed, A., Banakar, A., Li, G., and Javidan, S.M. (2025). Separating chickens\u2019 heads and legs in thermal images via object detection and machine learning models to predict avian influenza and Newcastle disease. Animals, 15.","DOI":"10.3390\/ani15081114"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guo, X., Wang, Y., Li, J., Li, Q., Zuo, Z., and Liu, Z. (2025). DOSA-YOLO: Improved model research for the detection of common chicken diseases using phenotypic features. Agriculture, 15.","DOI":"10.3390\/agriculture15191996"},{"key":"ref_25","first-page":"51","article-title":"Recognition method of sick yellow feather chicken based on head features","volume":"49","author":"Bi","year":"2018","journal-title":"Nongye Jixie Xuebao\/Trans. Chin. Soc. Agric. Mach."},{"key":"ref_26","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS), Neural Information Processing Systems Foundation."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/83.557356","article-title":"Properties and Performance of a Center\/Surround Retinex","volume":"6","author":"Jobson","year":"1997","journal-title":"IEEE Trans. Image Process"},{"key":"ref_28","unstructured":"Cai, Y., Zhou, Y., Han, Q., Sun, J., Kong, X., Li, J., and Zhang, X. (2022). Reversible column networks. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yang, G., Wang, J., Nie, Z., Yang, H., and Yu, S. (2023). A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention. Agronomy, 13.","DOI":"10.3390\/agronomy13071824"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_31","first-page":"6105","article-title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","volume":"Volume 97","author":"Tan","year":"2019","journal-title":"Proceedings of the 36th International Conference on Machine Learning (ICML)"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, J., Xu, F., and Song, J. (2019). Improvement of YOLOv3 algorithm in workpiece detection. 2019 IEEE 9th Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE.","DOI":"10.1109\/CYBER46603.2019.9066490"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., and Guo, X. (2022). Real-time vehicle detection based on improved YOLOv5. Sustainability, 14.","DOI":"10.3390\/su141912274"},{"key":"ref_34","first-page":"e13991","article-title":"Traffic object detection using YOLOv12","volume":"12","author":"Chen","year":"2025","journal-title":"Open Access Libr. J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024). DETRs Beat YOLOs on Real-Time Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_36","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","first-page":"10096","article-title":"EfficientNetV2: Smaller Models and Faster Training","volume":"Volume 139","author":"Tan","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning (ICML)"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019). Searching for MobileNetV3. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/16\/16\/2559\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T04:23:49Z","timestamp":1787113429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/16\/16\/2559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,17]]},"references-count":40,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["ani16162559"],"URL":"https:\/\/doi.org\/10.3390\/ani16162559","relation":{},"ISSN":["2076-2615"],"issn-type":[{"value":"2076-2615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,17]]}}}