{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T03:03:18Z","timestamp":1784516598175,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Province Rural Revitalization Strategy Special Project","award":["2025TS-3"],"award-info":[{"award-number":["2025TS-3"]}]},{"name":"Characteristic Innovation Project of Universities in Guangdong Province","award":["2025KTSCX011"],"award-info":[{"award-number":["2025KTSCX011"]}]},{"name":"Guangzhou City Key R&D Program","award":["2023B03J1363"],"award-info":[{"award-number":["2023B03J1363"]}]},{"name":"Guangzhou City Key R&D Program","award":["2025B01J0001"],"award-info":[{"award-number":["2025B01J0001"]}]},{"name":"Guangdong Province Facility and Equipment (Livestock Breeding) Key Technology Research and Development Innovation Team Project","award":["2024CXTD02"],"award-info":[{"award-number":["2024CXTD02"]}]},{"name":"Guangdong Province Facility and Equipment (Livestock Breeding) Key Technology Research and Development Innovation Team Project","award":["2025CXTD02"],"award-info":[{"award-number":["2025CXTD02"]}]},{"name":"Guangdong Province Agricultural Science and Technology Social Service Achievements Integration Demonstration Project","award":["20230201"],"award-info":[{"award-number":["20230201"]}]},{"name":"SCAU-Wens Science and Technology Innovation Center (Achievement Incubation) Project","award":["WS-HN-GCZB-202511-697"],"award-info":[{"award-number":["WS-HN-GCZB-202511-697"]}]},{"name":"Shanwei City Science and Technology Program","award":["2023A009"],"award-info":[{"award-number":["2023A009"]}]},{"name":"Guangzhou City Basic and Applied Basic Research Project","award":["2023A04J0752"],"award-info":[{"award-number":["2023A04J0752"]}]},{"name":"Humanities and Social Sciences Research Project of the Ministry of Education","award":["24YJA760032"],"award-info":[{"award-number":["24YJA760032"]}]},{"name":"Educational Research Project of Guangdong Province","award":["2024GXJK374"],"award-info":[{"award-number":["2024GXJK374"]}]},{"name":"Youth S&T Talent Support Program of Guangdong Provincial Association for Science and Technology","award":["SKXRC2025521"],"award-info":[{"award-number":["SKXRC2025521"]}]},{"name":"Scientific and Technological Innovation Strategic Program of Guangdong Academy of Agricultural Sciences","award":["ZX202402"],"award-info":[{"award-number":["ZX202402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Continuous sensing of group feeding demand is needed to support feeding decisions on large-scale pig farms. In dense group-housing environments, crowding, occlusion, and variation in posture around the trough can reduce the stability of visual feeding-status recognition. Feeding schedules also commonly rely on fixed periods or historical patterns, limiting their ability to assess whether daily feeding-behavior signals are aligned with group demand. Here, we propose a video-based workflow for sensing group feeding activity and conducting an offline replay evaluation of feeding-schedule allocation rules in group-housed pigs. Using synchronized feeding videos and feeder records, we developed a workflow that integrates rotated object detection, visual feeding intensity calculation, time-period-level construction of video-derived feeding demand, and feeding strategy evaluation. Rotated bounding-box detection achieved an mAP50-95 of 0.908 for feeding recognition, supporting its feasibility for feeding\/non-feeding recognition in this dense-pen scene. Visual feeding intensity derived from model outputs was most strongly correlated with actual feed intake increment under a 20 min aggregation window with no time lag (r = 0.80). In the evaluation based on video-derived feeding demand, the video-perceived hybrid strategy reduced the peak time difference to 14.51 min, achieved a synchronization correlation of 0.611, and reduced the mismatch rate to 5.13%. These findings suggest that video-based behavioral signals can serve as a group-level proxy for feeding demand and can support offline evaluation of behavior-aware feeding schedules. However, the demand and strategy analyses were based on a single pen observed for 72 h, and validation across additional pens, batches, age stages, seasons, and closed-loop feeding conditions is required before practical deployment.<\/jats:p>","DOI":"10.3390\/ani16142219","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T02:32:08Z","timestamp":1784514728000},"page":"2219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Video-Based Feeding Demand Sensing and Offline Feeding Strategy Evaluation in Group-Housed Pigs: A Single-Pen Proof-of-Concept Study Using Oriented Object Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Xinyuan","family":"He","sequence":"first","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijia","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7355-8430","authenticated-orcid":false,"given":"Guoxing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhua","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enli","family":"Lyu","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziwei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8362-5481","authenticated-orcid":false,"given":"Yizhi","family":"Luo","sequence":"additional","affiliation":[{"name":"Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiong","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104530","DOI":"10.1016\/j.livsci.2021.104530","article-title":"An overview of the current trends in precision pig farming technologies","volume":"249","author":"Tzanidakis","year":"2021","journal-title":"Livest. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rosa, G.J.M. (2021). Grand Challenge in Precision Livestock Farming. Front. Anim. Sci., 2.","DOI":"10.3389\/fanim.2021.650324"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jacobs, P., and van Erp-van der Kooij, E. (2021). Precision pig farming. Precision Technology and Sensor Applications for Livestock Farming and Companion Animals, Brill|Wageningen Academic.","DOI":"10.3920\/978-90-8686-917-6"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Siegford, J.M. (2024). Precision livestock farming and technology in pig husbandry. Advances in Pig Welfare, Elsevier.","DOI":"10.1016\/B978-0-323-85676-8.00015-8"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Burns, R.T., and Spaji\u0107, R. (2024). Precision Livestock Farming in Swine Production. Tracing the Domestic Pig, IntechOpen.","DOI":"10.5772\/intechopen.114845"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"s360","DOI":"10.1017\/S1751731120001469","article-title":"Precision livestock farming: Real-time estimation of daily protein deposition in growing-finishing pigs","volume":"14","author":"Remus","year":"2020","journal-title":"Animal"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.biosystemseng.2021.08.027","article-title":"Bayesian comparison of models for precision feeding and management in growing-finishing pigs","volume":"211","author":"Misiura","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1017\/S1751731120000798","article-title":"Improving the estimation of amino acid requirements to maximize nitrogen retention in precision feeding for growing-finishing pigs","volume":"14","author":"Remus","year":"2020","journal-title":"Animal"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pomar, C., Andretta, I., and Remus, A. (2021). Feeding Strategies to Reduce Nutrient Losses and Improve the Sustainability of Growing Pigs. Front. Vet. Sci., 8.","DOI":"10.3389\/fvets.2021.742220"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101763","DOI":"10.1016\/j.animal.2026.101763","article-title":"Evaluating a precision feeding decision support system for improving growth performance of growing-finishing pigs on a commercial farm","volume":"20","author":"Sacanell","year":"2026","journal-title":"Animal"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"txaa177","DOI":"10.1093\/tas\/txaa177","article-title":"Feeding behavior of growing and finishing pigs fed different dietary threonine levels in a group-phase feeding and individual precision feeding system","volume":"4","author":"Remus","year":"2020","journal-title":"Transl. Anim. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gomes, B.C.K., Andretta, I., Valk, M., Pomar, C., Hauschild, L., Fraga, A.Z., Kipper, M., Trevizan, L., and Remus, A. (2021). Prandial Correlations and Structure of the Ingestive Behavior of Pigs in Precision Feeding Programs. Animals, 11.","DOI":"10.3390\/ani11102998"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100251","DOI":"10.1016\/j.animal.2021.100251","article-title":"A novel feeding behavior index integrating several components of the feeding behavior of finishing pigs","volume":"15","author":"Salgado","year":"2021","journal-title":"Animal"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104650","DOI":"10.1016\/j.livsci.2021.104650","article-title":"Elucidating the impact of feeding behaviour on body composition in finishing pigs fed ad libitum using an integrative feeding behaviour index","volume":"251","author":"Salgado","year":"2021","journal-title":"Livest. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105383","DOI":"10.1016\/j.applanim.2021.105383","article-title":"The potential of feeding patterns to assess generic welfare in growing-finishing pigs","volume":"241","author":"Bus","year":"2021","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100351","DOI":"10.1016\/j.animal.2021.100351","article-title":"Evaluation of feeding behaviour traits to predict efficiency traits in pigs using partial least square regression","volume":"15","author":"Ewaoluwagbemiga","year":"2021","journal-title":"Animal"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Santiago, K.G., Kim, S.-H., Lopez, B.I., Lee, D.-H., Cho, Y.-G., Song, Y.-N., and Seo, K.-S. (2021). Estimation of Genetic Parameters for Feeding Pattern Traits and Its Relationship to Feed Efficiency and Production Traits in Duroc Pigs. Agriculture, 11.","DOI":"10.3390\/agriculture11090850"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"txae051","DOI":"10.1093\/tas\/txae051","article-title":"Online feeding behavior monitoring of individual group-housed grow-finish pigs using a low-frequency RFID electronic feeding system","volume":"8","author":"Funk","year":"2024","journal-title":"Transl. Anim. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105642","DOI":"10.1016\/j.compag.2020.105642","article-title":"Recognition of feeding behaviour of pigs and determination of feeding time of each pig by a video-based deep learning method","volume":"176","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.biosystemseng.2020.06.013","article-title":"Automatic recognition of feeding and foraging behaviour in pigs using deep learning","volume":"197","author":"Alameer","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105391","DOI":"10.1016\/j.compag.2020.105391","article-title":"Automatically detecting pig position and posture by 2D camera imaging and deep learning","volume":"174","author":"Riekert","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106213","DOI":"10.1016\/j.compag.2021.106213","article-title":"Model selection for 24\/7 pig position and posture detection by 2D camera imaging and deep learning","volume":"187","author":"Riekert","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shao, H., Pu, J., and Mu, J. (2021). Pig-Posture Recognition Based on Computer Vision: Dataset and Exploration. Animals, 11.","DOI":"10.3390\/ani11051295"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13665","DOI":"10.1038\/s41598-020-70688-6","article-title":"Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs","volume":"10","author":"Alameer","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.biosystemseng.2020.04.007","article-title":"A computer vision-based method for spatial-temporal action recognition of tail-biting behaviour in group-housed pigs","volume":"195","author":"Liu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.biosystemseng.2022.10.002","article-title":"Automated detection and quantification of contact behaviour in pigs using deep learning","volume":"224","author":"Alameer","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"H\u00e5kansson, F., and Jensen, D.B. (2023). Automatic monitoring and detection of tail-biting behavior in groups of pigs using video-based deep learning methods. Front. Vet. Sci., 9.","DOI":"10.3389\/fvets.2022.1099347"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108710","DOI":"10.1016\/j.compag.2024.108710","article-title":"A time-series neural network for pig feeding behavior recognition and dangerous detection from videos","volume":"218","author":"Zhang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","first-page":"63","article-title":"Advancing precision livestock farming in pigs through markerless pose estimation: A comparison between DeepLabCut and SLEAP","volume":"16","author":"Nasser","year":"2025","journal-title":"Anim. Front."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101545","DOI":"10.1016\/j.atech.2025.101545","article-title":"3D deep learning identifies pigs using point cloud data during different growth stages of grow-finish pigs","volume":"12","author":"Paudel","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"128466","DOI":"10.1016\/j.eswa.2025.128466","article-title":"Housed pig identification and tracking for precision livestock farming","volume":"293","author":"Compte","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7778","DOI":"10.1109\/TPAMI.2021.3117983","article-title":"Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges","volume":"44","author":"Ding","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021, January 10\u201317). Oriented R-CNN for Object Detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_35","first-page":"3163","article-title":"R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object","volume":"35","author":"Yang","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_36","unstructured":"Wang, W. (2026, January 13). X-AnyLabeling: Advanced Auto Labeling Solution with Added Features. GitHub Repository. Available online: https:\/\/github.com\/CVHub520\/X-AnyLabeling."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1007\/s11263-022-01593-w","article-title":"On the Arbitrary-Oriented Object Detection: Classification Based Approaches Revisited","volume":"130","author":"Yang","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"86057","DOI":"10.1109\/ACCESS.2022.3197588","article-title":"One-Stage Arbitrary-Oriented Object Detection with Prediction Decoupling","volume":"10","author":"Xiong","year":"2022","journal-title":"IEEE Access"},{"key":"ref_39","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":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111661","DOI":"10.1016\/j.compag.2026.111661","article-title":"PigInstance: An efficient Pig Instance segmentation framework for intelligent pig farming","volume":"247","author":"Lin","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","first-page":"190","article-title":"Pig Behavior Recognition in a Commercial Swine Barn Using Video Based Deep Learning","volume":"26","author":"Lee","year":"2025","journal-title":"J. Korea Acad.-Ind. Coop. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Melfsen, A., Lepsien, A., Bosselmann, J., Koschmider, A., and Hartung, E. (2023). Describing Behavior Sequences of Fattening Pigs Using Process Mining on Video Data and Automated Pig Behavior Recognition. Agriculture, 13.","DOI":"10.3390\/agriculture13081639"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Low, B.E., Cho, Y., Lee, B., and Yi, M.Y. (2022). Playing Behavior Classification of Group-Housed Pigs Using a Deep CNN-LSTM Network. Sustainability, 14.","DOI":"10.3390\/su142316181"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111829","DOI":"10.1016\/j.compag.2026.111829","article-title":"PigTracker: Sequential-vertices-based tracking framework for intelligent pig farming","volume":"248","author":"Lin","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"101163","DOI":"10.1016\/j.animal.2024.101163","article-title":"Pinpointing pigs: Performance and challenges of an ultra-wideband real-time location system for tracking growing-finishing pigs under practical conditions","volume":"18","author":"Knoll","year":"2024","journal-title":"Animal"}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/16\/14\/2219\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T02:59:42Z","timestamp":1784516382000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/16\/14\/2219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,17]]},"references-count":45,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["ani16142219"],"URL":"https:\/\/doi.org\/10.3390\/ani16142219","relation":{},"ISSN":["2076-2615"],"issn-type":[{"value":"2076-2615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,17]]}}}