{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T17:12:52Z","timestamp":1784221972011,"version":"3.55.0"},"reference-count":263,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T00:00:00Z","timestamp":1613865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"USDA Agricultural Research Service Cooperative Agreement","award":["58-6064-015"],"award-info":[{"award-number":["58-6064-015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic\/instance segmentation, pose estimation, and tracking. Cattle, sheep\/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.<\/jats:p>","DOI":"10.3390\/s21041492","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T22:04:15Z","timestamp":1613945055000},"page":"1492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":164,"title":["Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7624-8051","authenticated-orcid":false,"given":"Guoming","family":"Li","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanbo","family":"Huang","sequence":"additional","affiliation":[{"name":"Agricultural Research Service, Genetics and Sustainable Agriculture Unit, United States Department of Agriculture, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4112-9647","authenticated-orcid":false,"given":"Zhiqian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Mississippi State University, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"suffix":"Jr.","given":"Gary D.","family":"Chesser","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph L.","family":"Purswell","sequence":"additional","affiliation":[{"name":"Agricultural Research Service, Poultry Research Unit, United States Department of Agriculture, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Linhoss","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7809-6514","authenticated-orcid":false,"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Animal Science, The University of Tennessee, Knoxville, TN 37996, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20260","DOI":"10.1073\/pnas.1116437108","article-title":"Global food demand and the sustainable intensification of agriculture","volume":"108","author":"Tilman","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","unstructured":"McLeod, A. (2011). World Livestock 2011-Livestock in Food Security, Food and Agriculture Organization of the United Nations (FAO)."},{"key":"ref_3","first-page":"30","article-title":"Livestock and livestock product trends by 2050: A review","volume":"4","author":"Yitbarek","year":"2019","journal-title":"Int. J. Anim. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5746","DOI":"10.3168\/jds.2019-17804","article-title":"Symposium review: Considerations for the future of dairy cattle housing: An animal welfare perspective","volume":"103","author":"Beaver","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1093\/ajae\/aas090","article-title":"Is there a farm labor shortage?","volume":"95","author":"Hertz","year":"2013","journal-title":"Am. J. Agric. Econ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.biosystemseng.2013.06.004","article-title":"Development of an early warning system for a broiler house using computer vision","volume":"116","author":"Kashiha","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_7","unstructured":"Werner, A., and Jarfe, A. (2003). Programme Book of the Joint Conference of ECPA-ECPLF, Wageningen Academic Publishers."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1017\/S175173111900199X","article-title":"Precision livestock farming: Building \u2018digital representations\u2019 to bring the animals closer to the farmer","volume":"13","author":"Norton","year":"2019","journal-title":"Animal"},{"key":"ref_9","first-page":"1","article-title":"Precision livestock farming: An international review of scientific and commercial aspects","volume":"5","author":"Banhazi","year":"2012","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3389\/fsufs.2018.00031","article-title":"Novel monitoring systems to obtain dairy cattle phenotypes associated with sustainable production","volume":"2","author":"Bell","year":"2018","journal-title":"Front. Sustain. Food Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105333","DOI":"10.1016\/j.compag.2020.105333","article-title":"Assessment of layer pullet drinking behaviors under selectable light colors using convolutional neural network","volume":"172","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105596","DOI":"10.1016\/j.compag.2020.105596","article-title":"Analysis of feeding and drinking behaviors of group-reared broilers via image processing","volume":"175","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","first-page":"184","article-title":"A review on computer vision systems in monitoring of poultry: A welfare perspective","volume":"4","author":"Okinda","year":"2020","journal-title":"Artif. Intell. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_15","first-page":"1","article-title":"Deep learning for computer vision: A brief review","volume":"13","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_17","first-page":"2352","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"NeCom"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.34133\/2020\/4152816","article-title":"Convolutional neural networks for image-based high-throughput plant phenotyping: A review","volume":"2020","author":"Jiang","year":"2020","journal-title":"Plant Phenomics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1017\/S0021859618000436","article-title":"A review of the use of convolutional neural networks in agriculture","volume":"156","author":"Kamilaris","year":"2018","journal-title":"J. Agric. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gikunda, P.K., and Jouandeau, N. (2019, January 16\u201317). State-of-the-art convolutional neural networks for smart farms: A review. Proceedings of the Intelligent Computing-Proceedings of the Computing Conference, London, UK.","DOI":"10.1007\/978-3-030-22871-2_53"},{"key":"ref_22","unstructured":"Food and Agriculture Organization of the United States (2020, October 27). Livestock Statistics\u2014Concepts, Definition, and Classifications. Available online: http:\/\/www.fao.org\/economic\/the-statistics-division-ess\/methodology\/methodology-systems\/livestock-statistics-concepts-definitions-and-classifications\/en\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: A probabilistic model for information storage and organization in the brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol. Rev."},{"key":"ref_24","unstructured":"Werbos, P.J. (1994). The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting, John Wiley & Sons."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1985). Learning Internal Representations by Error Propagation, California Univ San Diego La Jolla Inst for Cognitive Science.","DOI":"10.21236\/ADA164453"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fukushima, K., and Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. Competition and Cooperation in Neural Nets, Springer.","DOI":"10.1007\/978-3-642-46466-9_18"},{"key":"ref_27","first-page":"541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"NeCom"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 1). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_29","first-page":"1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"NeCom"},{"key":"ref_30","unstructured":"Salakhutdinov, R., and Hinton, G. (2009, January 15). Deep boltzmann machines. Proceedings of the 12th Artificial Intelligence and Statistics, Clearwater Beach, FL, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Raina, R., Madhavan, A., and Ng, A.Y. (2009, January 14). Large-scale deep unsupervised learning using graphics processors. Proceedings of the 26th Annual International Conference on Machine Learning, New York, NY, USA.","DOI":"10.1145\/1553374.1553486"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_33","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_36","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201329). Densely connected convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., and Clark, C. (2020, January 20\u201321). BiLSTM-based individual cattle identification for automated precision livestock farming. Proceedings of the 16th International Conference on Automation Science and Engineering (CASE), Hong Kong, China.","DOI":"10.1109\/CASE48305.2020.9217026"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Psota, E.T., Schmidt, T., Mote, B., and C P\u00e9rez, L. (2020). Long-term tracking of group-housed livestock using keypoint detection and MAP estimation for individual animal identification. Sensors, 20.","DOI":"10.3390\/s20133670"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"105150","DOI":"10.1016\/j.compag.2019.105150","article-title":"Outdoor animal tracking combining neural network and time-lapse cameras","volume":"168","author":"Bonneau","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10628","DOI":"10.3168\/jds.2020-18288","article-title":"Accurate detection of lameness in dairy cattle with computer vision: A new and individualized detection strategy based on the analysis of the supporting phase","volume":"103","author":"Kang","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/01431161.2019.1624858","article-title":"Cattle detection and counting in UAV images based on convolutional neural networks","volume":"41","author":"Shao","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tu, S., Liu, H., Li, J., Huang, J., Li, B., Pang, J., and Xue, Y. (2020, January 18). Instance segmentation based on mask scoring R-CNN for group-housed pigs. Proceedings of the International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China.","DOI":"10.1109\/ICCEA50009.2020.00105"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, D., Zhang, K., Li, Z., and Chen, Y. (2020). A spatiotemporal convolutional network for multi-behavior recognition of pigs. Sensors, 20.","DOI":"10.3390\/s20082381"},{"key":"ref_48","first-page":"92","article-title":"Image-based individual cow recognition using body patterns","volume":"11","author":"Bello","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.compind.2018.02.016","article-title":"Towards on-farm pig face recognition using convolutional neural networks","volume":"98","author":"Hansen","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Huang, M.-H., Lin, E.-C., and Kuo, Y.-F. (2019, January 7\u201310). Determining the body condition scores of sows using convolutional neural networks. Proceedings of the ASABE Annual International Meeting, Boston, MA, USA.","DOI":"10.13031\/aim.201900915"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, G., Hui, X., Lin, F., and Zhao, Y. (2020). Developing and evaluating poultry preening behavior detectors via mask region-based convolutional neural network. Animals, 10.","DOI":"10.3390\/ani10101762"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.biosystemseng.2019.11.013","article-title":"Automatic recognition of lactating sow postures by refined two-stream RGB-D faster R-CNN","volume":"189","author":"Zhu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105754","DOI":"10.1016\/j.compag.2020.105754","article-title":"Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector","volume":"178","author":"Xudong","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"31681","DOI":"10.1109\/ACCESS.2019.2902724","article-title":"Sheep identification using a hybrid deep learning and bayesian optimization approach","volume":"7","author":"Salama","year":"2019","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sarwar, F., Griffin, A., Periasamy, P., Portas, K., and Law, J. (2018, January 27\u201330). Detecting and counting sheep with a convolutional neural network. Proceedings of the 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand.","DOI":"10.1109\/AVSS.2018.8639306"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Andrew, W., Greatwood, C., and Burghardt, T. (2017, January 22\u201329). Visual localisation and individual identification of holstein friesian cattle via deep learning. Proceedings of the IEEE International Conference on Computer Vision Workshops, Santa Rosa, CA, USA.","DOI":"10.1109\/ICCVW.2017.336"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6","DOI":"10.2527\/af.2017.0102","article-title":"General introduction to precision livestock farming","volume":"7","author":"Berckmans","year":"2017","journal-title":"Anim. Front."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Br\u00fcnger, J., Gentz, M., Traulsen, I., and Koch, R. (2020). Panoptic segmentation of individual pigs for posture recognition. Sensor, 20.","DOI":"10.3390\/s20133710"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Nasirahmadi, A., Sturm, B., Edwards, S., Jeppsson, K.-H., Olsson, A.-C., M\u00fcller, S., and Hensel, O. (2019). Deep learning and machine vision approaches for posture detection of individual pigs. Sensors, 19.","DOI":"10.3390\/s19173738"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, G., Shen, S., Wen, L., Luo, S., and Bo, L. (August, January 31). Efficient pig counting in crowds with keypoints tracking and spatial-aware temporal response filtering. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197211"},{"key":"ref_62","unstructured":"Song, C., and Rao, X. (August, January 29). Behaviors detection of pregnant sows based on deep learning. Proceedings of the ASABE Annual International Meeting, Detroit, MI, USA."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Li, Z., Ge, C., Shen, S., and Li, X. (2018, January 21\u201323). Cow individual identification based on convolutional neural network. Proceedings of the International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China.","DOI":"10.1145\/3302425.3302460"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"105300","DOI":"10.1016\/j.compag.2020.105300","article-title":"Automated cattle counting using Mask R-CNN in quadcopter vision system","volume":"171","author":"Xu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.compag.2018.09.030","article-title":"Dairy goat detection based on Faster R-CNN from surveillance video","volume":"154","author":"Wang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_66","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_67","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.biosystemseng.2018.10.005","article-title":"High-accuracy image segmentation for lactating sows using a fully convolutional network","volume":"176","author":"Yang","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.biosystemseng.2018.09.011","article-title":"Automatic recognition of sow nursing behaviour using deep learning-based segmentation and spatial and temporal features","volume":"175","author":"Yang","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"105548","DOI":"10.1016\/j.compag.2020.105548","article-title":"Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks","volume":"175","author":"Menezes","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"105386","DOI":"10.1016\/j.compag.2020.105386","article-title":"An adaptive pig face recognition approach using Convolutional Neural Networks","volume":"173","author":"Marsot","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"105706","DOI":"10.1016\/j.compag.2020.105706","article-title":"Automatic behavior recognition of group-housed goats using deep learning","volume":"177","author":"Jiang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"1550147720944030","DOI":"10.1177\/1550147720944030","article-title":"On-farm welfare monitoring system for goats based on Internet of Things and machine learning","volume":"16","author":"Rao","year":"2020","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.biosystemseng.2020.02.001","article-title":"Cow identification based on fusion of deep parts features","volume":"192","author":"Hu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.biosystemseng.2019.12.002","article-title":"Comparative study on poultry target tracking algorithms based on a deep regression network","volume":"190","author":"Fang","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"105528","DOI":"10.1016\/j.compag.2020.105528","article-title":"Automated sheep facial expression classification using deep transfer learning","volume":"175","author":"Noor","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Psota, E.T., Mittek, M., P\u00e9rez, L.C., Schmidt, T., and Mote, B. (2019). Multi-pig part detection and association with a fully-convolutional network. Sensors, 19.","DOI":"10.3390\/s19040852"},{"key":"ref_78","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_79","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.13031\/trans.13607","article-title":"Automatic monitoring of chicken movement and drinking time using convolutional neural networks","volume":"63","author":"Lin","year":"2020","journal-title":"Trans. Asabe"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhang, K., Li, D., Huang, J., and Chen, Y. (2020). Automated video behavior recognition of pigs using two-stream convolutional networks. Sensors, 20.","DOI":"10.3390\/s20041085"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, X., and Hu, Z. (2019, January 22\u201324). Cow tail detection method for body condition score using Faster R-CNN. Proceedings of the IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), Xi\u2032an, China.","DOI":"10.1109\/ICUSAI47366.2019.9124743"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ju, M., Choi, Y., Seo, J., Sa, J., Lee, S., Chung, Y., and Park, D. (2018). A Kinect-based segmentation of touching-pigs for real-time monitoring. Sensors, 18.","DOI":"10.3390\/s18061746"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhang, L., Gray, H., Ye, X., Collins, L., and Allinson, N. (2018). Automatic individual pig detection and tracking in surveillance videos. arXiv.","DOI":"10.3390\/s19051188"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"105707","DOI":"10.1016\/j.compag.2020.105707","article-title":"Using an EfficientNet-LSTM for the recognition of single cow\u2019s motion behaviours in a complicated environment","volume":"177","author":"Yin","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.biosystemseng.2020.03.013","article-title":"Assessment of dairy cow heat stress by monitoring drinking behaviour using an embedded imaging system","volume":"199","author":"Tsai","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_86","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_87","doi-asserted-by":"crossref","first-page":"105166","DOI":"10.1016\/j.compag.2019.105166","article-title":"Recognition of aggressive episodes of pigs based on convolutional neural network and long short-term memory","volume":"169","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1","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_89","doi-asserted-by":"crossref","unstructured":"Seo, J., Ahn, H., Kim, D., Lee, S., Chung, Y., and Park, D. (2020). EmbeddedPigDet\u2014fast and accurate pig detection for embedded board implementations. Appl. Sci., 10.","DOI":"10.3390\/app10082878"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, Y., Zhang, K., and Li, Z. (2019). Mounting behaviour recognition for pigs based on deep learning. Sensors, 19.","DOI":"10.3390\/s19224924"},{"key":"ref_91","first-page":"303","article-title":"Automated estrus detection for dairy cattle through neural networks and bounding box corner analysis","volume":"11","author":"Arago","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_92","unstructured":"Danish, M. (2018). Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network. [Master\u2019s Thesis, Technological University]."},{"key":"ref_93","unstructured":"Ter-Sarkisov, A., Ross, R., Kelleher, J., Earley, B., and Keane, M. (2018). Beef cattle instance segmentation using fully convolutional neural network. arXiv."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.compag.2018.11.002","article-title":"Feeding behavior recognition for group-housed pigs with the Faster R-CNN","volume":"155","author":"Yang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.biosystemseng.2020.04.005","article-title":"Automatic posture change analysis of lactating sows by action localisation and tube optimisation from untrimmed depth videos","volume":"194","author":"Zheng","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"108049","DOI":"10.1109\/ACCESS.2019.2933060","article-title":"Automated individual pig localisation, tracking and behaviour metric extraction using deep learning","volume":"7","author":"Cowton","year":"2019","journal-title":"IEEE Access"},{"key":"ref_97","unstructured":"Khan, A.Q., Khan, S., Ullah, M., and Cheikh, F.A. (2020, January 4\u20136). A bottom-up approach for pig skeleton extraction using rgb data. Proceedings of the International Conference on Image and Signal Processing, Marrakech, Morocco."},{"key":"ref_98","unstructured":"Li, X., Hu, Z., Huang, X., Feng, T., Yang, X., and Li, M. (2015, January 5\u20137). Cow body condition score estimation with convolutional neural networks. Proceedings of the IEEE 4th International Conference on Image, Vision and Computing (ICIVC), Xiamen, China."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"104982","DOI":"10.1016\/j.compag.2019.104982","article-title":"FLYOLOv3 deep learning for key parts of dairy cow body detection","volume":"166","author":"Jiang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Andrew, W., Greatwood, C., and Burghardt, T. (2019, January 4\u20138). Aerial animal biometrics: Individual friesian cattle recovery and visual identification via an autonomous uav with onboard deep inference. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Venetian Macao, Macau, China.","DOI":"10.1109\/IROS40897.2019.8968555"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Alvarez, J.R., Arroqui, M., Mangudo, P., Toloza, J., Jatip, D., Rodriguez, J.M., Teyseyre, A., Sanz, C., Zunino, A., and Machado, C. (2019). Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning and model ensembling techniques. Agronomy, 9.","DOI":"10.3390\/agronomy9020090"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"105627","DOI":"10.1016\/j.compag.2020.105627","article-title":"Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information","volume":"177","author":"Fuentes","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Ju, S., Erasmus, M.A., Reibman, A.R., and Zhu, F. (2020, January 29\u201331). Video tracking to monitor turkey welfare. Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe Plaza, NM, USA.","DOI":"10.1109\/SSIAI49293.2020.9094604"},{"key":"ref_104","unstructured":"Lee, S.K. (2020). Pig Pose Estimation Based on Extracted Data of Mask R-CNN with VGG Neural Network for Classifications. [Master\u2019s Thesis, South Dakota State University]."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Sa, J., Choi, Y., Lee, H., Chung, Y., Park, D., and Cho, J. (2019). Fast pig detection with a top-view camera under various illumination conditions. Symmetry, 11.","DOI":"10.3390\/sym11020266"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"8121","DOI":"10.1080\/01431161.2020.1734245","article-title":"Livestock classification and counting in quadcopter aerial images using Mask R-CNN","volume":"41","author":"Xu","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"105055","DOI":"10.1016\/j.compag.2019.105055","article-title":"On farm automatic sheep breed classification using deep learning","volume":"167","author":"Jwade","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.compag.2018.09.039","article-title":"Body condition estimation on cows from depth images using Convolutional Neural Networks","volume":"155","author":"Alvarez","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_109","unstructured":"Gonzalez, R.C., Woods, R.E., and Eddins, S.L. (2004). Digital Image Processing Using MATLAB, Pearson Education India."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"104884","DOI":"10.1016\/j.compag.2019.104884","article-title":"Real-time sow behavior detection based on deep learning","volume":"163","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_111","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_112","doi-asserted-by":"crossref","unstructured":"Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., and Clark, C. (2020, January 20\u201321). Data augmentation for deep learning based cattle segmentation in precision livestock farming. Proceedings of the 16th International Conference on Automation Science and Engineering (CASE), Hong Kong, China.","DOI":"10.1109\/CASE48305.2020.9216758"},{"key":"ref_113","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_114","doi-asserted-by":"crossref","first-page":"105580","DOI":"10.1016\/j.compag.2020.105580","article-title":"A computer vision approach for recognition of the engagement of pigs with different enrichment objects","volume":"175","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"10140","DOI":"10.3168\/jds.2018-16164","article-title":"Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score","volume":"102","author":"Yukun","year":"2019","journal-title":"J. Dairy Sci."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.10.064","article-title":"Deep learning framework for recognition of cattle using muzzle point image pattern","volume":"116","author":"Kumar","year":"2018","journal-title":"Measurement"},{"key":"ref_117","unstructured":"Zin, T.T., Phyo, C.N., Tin, P., Hama, H., and Kobayashi, I. Image technology based cow identification system using deep learning. Proceedings of the Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, China, 14\u201316 March 2018."},{"key":"ref_118","first-page":"192","article-title":"Multi target pigs tracking loss correction algorithm based on Faster R-CNN","volume":"11","author":"Sun","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2020.05.010","article-title":"Classification of drinking and drinker-playing in pigs by a video-based deep learning method","volume":"196","author":"Chen","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Barbedo, J.G.A., Koenigkan, L.V., Santos, T.T., and Santos, P.M. (2019). A study on the detection of cattle in UAV images using deep learning. Sensors, 19.","DOI":"10.20944\/preprints201912.0089.v1"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Kuan, C.Y., Tsai, Y.C., Hsu, J.T., Ding, S.T., and Lin, T.T. (2019, January 7\u201310). An imaging system based on deep learning for monitoring the feeding behavior of dairy cows. Proceedings of the ASABE Annual International Meeting, Boston, MA, USA.","DOI":"10.13031\/aim.201901469"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2018.01.023","article-title":"Automatic recognition of lactating sow postures from depth images by deep learning detector","volume":"147","author":"Zheng","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2003). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_124","unstructured":"Ard\u00f6, H., Guzhva, O., and Nilsson, M. (2016, January 4\u20138). A CNN-based cow interaction watchdog. Proceedings of the 23rd International Conference Pattern Recognition, Cancun, Mexico."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"107","DOI":"10.3389\/frobt.2018.00107","article-title":"Now you see me: Convolutional neural network based tracker for dairy cows","volume":"5","author":"Guzhva","year":"2018","journal-title":"Front. Robot. AI"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Yao, Y., Yu, H., Mu, J., Li, J., and Pu, H. (2020). Estimation of the gender ratio of chickens based on computer vision: Dataset and exploration. Entropy, 22.","DOI":"10.3390\/e22070719"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"104958","DOI":"10.1016\/j.compag.2019.104958","article-title":"Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming","volume":"165","author":"Qiao","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Kim, J., Chung, Y., Choi, Y., Sa, J., Kim, H., Chung, Y., Park, D., and Kim, H. (2017). Depth-based detection of standing-pigs in moving noise environments. Sensors, 17.","DOI":"10.3390\/s17122757"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.anbehav.2014.02.007","article-title":"Observer bias in animal behaviour research: Can we believe what we score, if we score what we believe?","volume":"90","author":"Tuyttens","year":"2014","journal-title":"Anim. Behav."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Bergamini, L., Porrello, A., Dondona, A.C., Del Negro, E., Mattioli, M., D\u2019alterio, N., and Calderara, S. (2018, January 26\u201329). Multi-views embedding for cattle re-identification. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Las Palmas de Gran Canaria, Spain.","DOI":"10.1109\/SITIS.2018.00036"},{"key":"ref_131","first-page":"1248","article-title":"Body condition score (BCS) segmentation and classification in dairy cows using R-CNN deep learning architecture","volume":"17","author":"Mustafa","year":"2019","journal-title":"Eur. J. Sci. Technol."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"105761","DOI":"10.1016\/j.compag.2020.105761","article-title":"Video analytic system for detecting cow structure","volume":"178","author":"Liu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_133","unstructured":"GitHub (2021, January 27). LabelImg. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"022031","DOI":"10.1088\/1742-6596\/1486\/2\/022031","article-title":"Pig target detection method based on SSD convolution network","volume":"1486","author":"Deng","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_135","unstructured":"MathWorks (2021, January 27). Get started with the Image Labeler. Available online: https:\/\/www.mathworks.com\/help\/vision\/ug\/get-started-with-the-image-labeler.html."},{"key":"ref_136","unstructured":"GitHub (2021, January 27). Sloth. Available online: https:\/\/github.com\/cvhciKIT\/sloth."},{"key":"ref_137","unstructured":"Columbia Engineering (2021, January 27). Video Annotation Tool from Irvine, California. Available online: http:\/\/www.cs.columbia.edu\/~vondrick\/vatic\/."},{"key":"ref_138","unstructured":"Apple Store (2021, January 27). Graphic for iPad. Available online: https:\/\/apps.apple.com\/us\/app\/graphic-for-ipad\/id363317633."},{"key":"ref_139","unstructured":"SUPERVISELY (2021, January 27). The leading platform for entire computer vision lifecycle. Available online: https:\/\/supervise.ly\/."},{"key":"ref_140","unstructured":"GitHub (2021, January 27). Labelme. Available online: https:\/\/github.com\/wkentaro\/labelme."},{"key":"ref_141","unstructured":"Oxford University Press (2021, January 27). VGG Image Annotator (VIA). Available online: https:\/\/www.robots.ox.ac.uk\/~vgg\/software\/via\/."},{"key":"ref_142","unstructured":"GitHub (2021, January 27). DeepPoseKit. Available online: https:\/\/github.com\/jgraving\/DeepPoseKit."},{"key":"ref_143","unstructured":"Mathis Lab (2021, January 27). DeepLabCut: A Software Package for Animal Pose Estimation. Available online: http:\/\/www.mousemotorlab.org\/deeplabcut."},{"key":"ref_144","unstructured":"GitHub (2021, January 27). KLT-Feature-Tracking. Available online: https:\/\/github.com\/ZheyuanXie\/KLT-Feature-Tracking."},{"key":"ref_145","unstructured":"Mangold (2021, January 27). Interact: The Software for Video-Based Research. Available online: https:\/\/www.mangold-international.com\/en\/products\/software\/behavior-research-with-mangold-interact."},{"key":"ref_146","unstructured":"MathWorks (2021, January 27). Video Labeler. Available online: https:\/\/www.mathworks.com\/help\/vision\/ref\/videolabeler-app.html."},{"key":"ref_147","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press Cambridge."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_149","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201323). Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"14711","DOI":"10.1007\/s11042-019-7344-7","article-title":"Individual identification of dairy cows based on convolutional neural networks","volume":"79","author":"Shen","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.biosystemseng.2019.11.017","article-title":"Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector","volume":"189","author":"Wu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.ifacol.2019.12.558","article-title":"Individual cattle identification using a deep learning based framework","volume":"52","author":"Qiao","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_154","unstructured":"GitHub (2021, January 27). AlexNet. Available online: https:\/\/github.com\/paniabhisek\/AlexNet."},{"key":"ref_155","unstructured":"GitHub (2021, January 27). LeNet-5. Available online: https:\/\/github.com\/activatedgeek\/LeNet-5."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Wang, K., Chen, C., and He, Y. (2020, January 18\u201321). Research on pig face recognition model based on keras convolutional neural network. Proceedings of the IOP Conference Series: Earth and Environmental Science, Osaka, Japan.","DOI":"10.1088\/1755-1315\/474\/3\/032030"},{"key":"ref_157","unstructured":"GitHub (2021, January 27). Googlenet. Available online: https:\/\/gist.github.com\/joelouismarino\/a2ede9ab3928f999575423b9887abd14."},{"key":"ref_158","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_159","unstructured":"GitHub (2021, January 27). Models. Available online: https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/slim\/nets."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, Inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_161","unstructured":"GitHub (2021, January 27). Inception-Resnet-v2. Available online: https:\/\/github.com\/transcranial\/inception-resnet-v2."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 22\u201325). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_163","unstructured":"GitHub (2021, January 27). TensorFlow-Xception. Available online: https:\/\/github.com\/kwotsin\/TensorFlow-Xception."},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_165","unstructured":"GitHub (2021, January 27). Pytorch-Mobilenet-v2. Available online: https:\/\/github.com\/tonylins\/pytorch-mobilenet-v2."},{"key":"ref_166","unstructured":"GitHub (2021, January 27). Keras-Applications. Available online: https:\/\/github.com\/keras-team\/keras-applications\/blob\/master\/keras_applications."},{"key":"ref_167","unstructured":"GitHub (2021, January 27). DenseNet. Available online: https:\/\/github.com\/liuzhuang13\/DenseNet."},{"key":"ref_168","unstructured":"GitHub (2021, January 27). Deep-Residual-Networks. Available online: https:\/\/github.com\/KaimingHe\/deep-residual-networks."},{"key":"ref_169","unstructured":"GitHub (2021, January 27). Tensorflow-Vgg. Available online: https:\/\/github.com\/machrisaa\/tensorflow-vgg."},{"key":"ref_170","unstructured":"GitHub (2021, January 27). Darknet. Available online: https:\/\/github.com\/pjreddie\/darknet."},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_172","unstructured":"GitHub (2021, January 27). Darknet19. Available online: https:\/\/github.com\/amazarashi\/darknet19."},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Liu, S., and Huang, D. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_175","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_176","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_177","unstructured":"GitHub (2021, January 27). RFBNet. Available online: https:\/\/github.com\/ruinmessi\/RFBNet."},{"key":"ref_178","unstructured":"GitHub (2021, January 27). Caffe. Available online: https:\/\/github.com\/weiliu89\/caffe\/tree\/ssd."},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Katamreddy, S., Doody, P., Walsh, J., and Riordan, D. (2018, January 3\u20136). Visual udder detection with deep neural networks. Proceedings of the 12th International Conference on Sensing Technology (ICST), Limerick, Ireland.","DOI":"10.1109\/ICSensT.2018.8603625"},{"key":"ref_180","unstructured":"GitHub (2021, January 27). Yolo-9000. Available online: https:\/\/github.com\/philipperemy\/yolo-9000."},{"key":"ref_181","unstructured":"GitHub (2021, January 27). YOLO_v2. Available online: https:\/\/github.com\/leeyoshinari\/YOLO_v2."},{"key":"ref_182","unstructured":"GitHub (2021, January 27). TinyYOLOv2. Available online: https:\/\/github.com\/simo23\/tinyYOLOv2."},{"key":"ref_183","unstructured":"GitHub (2021, January 27). Yolov3. Available online: https:\/\/github.com\/ultralytics\/yolov3."},{"key":"ref_184","unstructured":"GitHub (2021, January 27). Darknet. Available online: https:\/\/github.com\/AlexeyAB\/darknet."},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_186","unstructured":"GitHub (2021, January 27). Rcnn. Available online: https:\/\/github.com\/rbgirshick\/rcnn."},{"key":"ref_187","unstructured":"GitHub (2021, January 27). Py-Faster-Rcnn. Available online: https:\/\/github.com\/rbgirshick\/py-faster-rcnn."},{"key":"ref_188","unstructured":"GitHub (2021, January 27). Mask_RCNN. Available online: https:\/\/github.com\/matterport\/Mask_RCNN."},{"key":"ref_189","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 4\u20139). R-fcn: Object detection via region-based fully convolutional networks. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_190","unstructured":"GitHub (2021, January 27). R-FCN. Available online: https:\/\/github.com\/daijifeng001\/r-fcn."},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Chen, C. (2020, January 12\u201314). Design of sick chicken automatic detection system based on improved residual network. Proceedings of the IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.","DOI":"10.1109\/ITNEC48623.2020.9084666"},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_193","unstructured":"GitHub (2021, January 27). ResNeXt. Available online: https:\/\/github.com\/facebookresearch\/ResNeXt."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"104840","DOI":"10.1016\/j.compag.2019.05.049","article-title":"Automated pig counting using deep learning","volume":"163","author":"Tian","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast R-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s41095-019-0132-5","article-title":"Livestock detection in aerial images using a fully convolutional network","volume":"5","author":"Han","year":"2019","journal-title":"Comput. Vis. Media"},{"key":"ref_197","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 8\u201310). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_198","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Li, Y., Qi, H., Dai, J., Ji, X., and Wei, Y. (2017, January 21\u201326). Fully convolutional instance-aware semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.472"},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","article-title":"Erfnet: Efficient residual factorized convnet for real-time semantic segmentation","volume":"19","author":"Romera","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_202","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., and Wang, X. (2019, January 16\u201319). Mask scoring r-cnn. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"ref_203","unstructured":"Bitbucket (2021, January 27). Deeplab-Public-Ver2. Available online: https:\/\/bitbucket.org\/aquariusjay\/deeplab-public-ver2\/src\/master\/."},{"key":"ref_204","unstructured":"GitHub (2021, January 27). Erfnet_Pytorch. Available online: https:\/\/github.com\/Eromera\/erfnet_pytorch."},{"key":"ref_205","unstructured":"GitHub (2021, January 27). FCIS. Available online: https:\/\/github.com\/msracver\/FCIS."},{"key":"ref_206","unstructured":"GitHub (2021, January 27). Pytorch-Fcn. Available online: https:\/\/github.com\/wkentaro\/pytorch-fcn."},{"key":"ref_207","unstructured":"GitHub (2021, January 27). Pysemseg. Available online: https:\/\/github.com\/petko-nikolov\/pysemseg."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Seo, J., Sa, J., Choi, Y., Chung, Y., Park, D., and Kim, H. (2019, January 17\u201320). A yolo-based separation of touching-pigs for smart pig farm applications. Proceedings of the 21st International Conference on Advanced Communication Technology (ICACT), Phoenix Park, PyeongChang, Korea.","DOI":"10.23919\/ICACT.2019.8701968"},{"key":"ref_209","unstructured":"GitHub (2021, January 27). Maskscoring_Rcnn. Available online: https:\/\/github.com\/zjhuang22\/maskscoring_rcnn."},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Toshev, A., and Szegedy, C. (2014, January 24\u201327). Deeppose: Human pose estimation via deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.214"},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1038\/s41593-018-0209-y","article-title":"DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning","volume":"21","author":"Mathis","year":"2018","journal-title":"Nat. Neurosci."},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Bulat, A., and Tzimiropoulos, G. (2016, January 8\u201316). Human pose estimation via convolutional part heatmap regression. Proceedings of the European Conference on Computer Vision, Amesterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_44"},{"key":"ref_213","unstructured":"Wei, S.-E., Ramakrishna, V., Kanade, T., and Sheikh, Y. (July, January 26). Convolutional pose machines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., and Deng, J. (2016, January 8\u201316). Stacked hourglass networks for human pose estimation. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"ref_215","doi-asserted-by":"crossref","unstructured":"GitHub (2021, January 27). Human-Pose-Estimation. Available online: https:\/\/github.com\/1adrianb\/human-pose-estimation.","DOI":"10.1007\/978-3-030-03243-2_584-1"},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"104885","DOI":"10.1016\/j.compag.2019.104885","article-title":"Deep cascaded convolutional models for cattle pose estimation","volume":"164","author":"Li","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_217","unstructured":"GitHub (2021, January 27). Convolutional-Pose-Machines-Release. Available online: https:\/\/github.com\/shihenw\/convolutional-pose-machines-release."},{"key":"ref_218","unstructured":"GitHub (2021, January 27). HyperStackNet. Available online: https:\/\/github.com\/neherh\/HyperStackNet."},{"key":"ref_219","unstructured":"GitHub (2021, January 27). DeepLabCut. Available online: https:\/\/github.com\/DeepLabCut\/DeepLabCut."},{"key":"ref_220","unstructured":"GitHub (2021, January 27). Deeppose. Available online: https:\/\/github.com\/mitmul\/deeppose."},{"key":"ref_221","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 8\u201313). Two-stream convolutional networks for action recognition in videos. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., and Darrell, T. (2015, January 8\u201310). Long-term recurrent convolutional networks for visual recognition and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"Held, D., Thrun, S., and Savarese, S. (2016, January 8\u201316). Learning to track at 100 fps with deep regression networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"ref_224","unstructured":"GitHub (2021, January 27). GOTURN. Available online: https:\/\/github.com\/davheld\/GOTURN."},{"key":"ref_225","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., and He, K. (November, January 27). Slowfast networks for video recognition. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_226","unstructured":"GitHub (2021, January 27). SlowFast. Available online: https:\/\/github.com\/facebookresearch\/SlowFast."},{"key":"ref_227","unstructured":"GitHub (2021, January 27). ActionRecognition. Available online: https:\/\/github.com\/jerryljq\/ActionRecognition."},{"key":"ref_228","unstructured":"GitHub (2021, January 27). Pytorch-Gve-Lrcn. Available online: https:\/\/github.com\/salaniz\/pytorch-gve-lrcn."},{"key":"ref_229","unstructured":"GitHub (2021, January 27). Inception-Inspired-LSTM-for-Video-Frame-Prediction. Available online: https:\/\/github.com\/matinhosseiny\/Inception-inspired-LSTM-for-Video-frame-Prediction."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1017\/S1751731120001676","article-title":"A machine vision system to detect and count laying hens in battery cages","volume":"14","author":"Geffen","year":"2020","journal-title":"Animal"},{"key":"ref_231","unstructured":"Alpaydin, E. (2020). Introduction to Machine Learning, MIT Press."},{"key":"ref_232","unstructured":"Fine, T.L. (2006). Feedforward Neural Network Methodology, Springer Science & Business Media."},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"62448","DOI":"10.1109\/ACCESS.2020.2981496","article-title":"Dairy goat image generation based on improved-self-attention generative adversarial networks","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_235","unstructured":"Yu, T., and Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. arXiv."},{"key":"ref_236","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A stochastic approximation method","volume":"22","author":"Robbins","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","article-title":"On the momentum term in gradient descent learning algorithms","volume":"12","author":"Qian","year":"1999","journal-title":"Neural Netw."},{"key":"ref_239","first-page":"1","article-title":"Neural networks for machine learning","volume":"264","author":"Hinton","year":"2012","journal-title":"Coursera Video Lect."},{"key":"ref_240","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_241","unstructured":"Zhang, M., Lucas, J., Ba, J., and Hinton, G.E. (2019, January 8\u201314). Lookahead optimizer: K steps forward, 1 step back. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_242","unstructured":"Zeiler, M.D. (2012). Adadelta: An adaptive learning rate method. arXiv."},{"key":"ref_243","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_244","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_245","first-page":"1341","article-title":"The lack of a priori distinctions between learning algorithms","volume":"8","author":"Wolpert","year":"1996","journal-title":"NeCom"},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-validatory choice and assessment of statistical predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","article-title":"Taking the human out of the loop: A review of Bayesian optimization","volume":"104","author":"Shahriari","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"1850023","DOI":"10.1142\/S0218001418500234","article-title":"Automatic recognition of flock behavior of chickens with convolutional neural network and kinect sensor","volume":"32","author":"Pu","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_249","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.procs.2015.07.525","article-title":"Cattle race classification using gray level co-occurrence matrix convolutional neural networks","volume":"59","author":"Santoni","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_250","unstructured":"ImageNet (2021, February 02). Image Classification on ImageNet. Available online: https:\/\/paperswithcode.com\/sota\/image-classification-on-imagenet."},{"key":"ref_251","unstructured":"USDA Foreign Agricultural Service (2020, November 16). Livestock and Poultry: World Markets and Trade, Available online: https:\/\/apps.fas.usda.gov\/psdonline\/circulars\/livestock_poultry.pdf."},{"key":"ref_252","doi-asserted-by":"crossref","unstructured":"Rowe, E., Dawkins, M.S., and Gebhardt-Henrich, S.G. (2019). A systematic review of precision livestock farming in the poultry sector: Is technology focussed on improving bird welfare?. Animals, 9.","DOI":"10.3390\/ani9090614"},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cvfa.2018.11.002","article-title":"Lying time and its importance to the dairy cow: Impact of stocking density and time budget stresses","volume":"35","author":"Krawczel","year":"2019","journal-title":"Vet. Clin. Food Anim. Pract."},{"key":"ref_254","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.applanim.2015.10.002","article-title":"Stocking density affects welfare indicators of growing pigs of different group sizes after regrouping","volume":"174","author":"Fu","year":"2016","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.japr.2020.01.002","article-title":"Effects of antibiotic-free diet and stocking density on male broilers reared to 35 days of age. Part 2: Feeding and drinking behaviours of broilers","volume":"29","author":"Li","year":"2020","journal-title":"J. Appl. Poult. Res."},{"key":"ref_256","unstructured":"University of BRISTOL (2021, January 27). Dataset. Available online: https:\/\/data.bris.ac.uk\/data\/dataset."},{"key":"ref_257","unstructured":"GitHub (2021, January 27). Aerial-Livestock-Dataset. Available online: https:\/\/github.com\/hanl2010\/Aerial-livestock-dataset\/releases."},{"key":"ref_258","unstructured":"GitHub (2021, January 27). Counting-Pigs. Available online: https:\/\/github.com\/xixiareone\/counting-pigs."},{"key":"ref_259","unstructured":"Naemura Lab (2021, January 27). Catte Dataset. Available online: http:\/\/bird.nae-lab.org\/cattle\/."},{"key":"ref_260","unstructured":"Universitat Hohenheim (2021, January 27). Supplementary Material. Available online: https:\/\/wi2.uni-hohenheim.de\/analytics."},{"key":"ref_261","unstructured":"Google Drive (2021, January 27). Classifier. Available online: https:\/\/drive.google.com\/drive\/folders\/1eGq8dWGL0I3rW2B9eJ_casH0_D3x7R73."},{"key":"ref_262","unstructured":"GitHub (2021, January 27). Database. Available online: https:\/\/github.com\/MicaleLee\/Database."},{"key":"ref_263","unstructured":"PSRG (2021, January 27). 12-Animal-Tracking. Available online: http:\/\/psrg.unl.edu\/Projects\/Details\/12-Animal-Tracking."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1492\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:27:28Z","timestamp":1760160448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,21]]},"references-count":263,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041492"],"URL":"https:\/\/doi.org\/10.3390\/s21041492","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,21]]}}}