Benjamin M, Yik S. 2019. Precision livestock farming in swine welfare: a review for swine practitioners. Animals, 9, 133.

Berckmans D. 2017. General introduction to precision livestock farming. Animal Frontiers, 7, 6-11.

Bergamini L, Pini S, Simoni A, Vezzani R, Calderara S, Eath R B, Fisher R B.2021. Extracting accurate long-term behavior changes from a large pig dataset. 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021, 524-533.

Chen C, Zhu W, Oczak M, Maschat K, Baumgartner J, Larsen M L V, Norton T. 2020. A computer vision approach for recognition of the engagement of pigs with different enrichment objects. Computers and electronics in agriculture, 175, 105580.

Fang Y, Wang W, Xie B, Sun Q, Wu L, Wang X, Huang T, Wang X, Cao Y.2023. Eva: Exploring the limits of masked visual representation learning at scale. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 19358-19369.

Fernandes A F A, Dórea J R R, Rosa G J D M. 2020. Image analysis and computer vision applications in animal sciences: an overview. Frontiers in Veterinary Science, 7, 551269.

Gebru T, Morgenstern J, Vecchione B, Vaughan J W, Wallach H, Iii H D, Crawford K. 2021. Datasheets for datasets. Communications of the ACM, 64, 86-92.

He Y, Tiezzi F, Howard J, Maltecca C. 2021. Predicting body weight in growing pigs from feeding behavior data using machine learning algorithms. Computers and electronics in agriculture, 184, 106085.

Ke L, Ye M, Danelljan M, Tai Y-W, Tang C-K, Yu F. 2024. Segment anything in high quality. Advances in Neural Information Processing Systems, 36,

Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg A C, Lo W-Y.2023. Segment anything. Proceedings of the IEEE/CVF International Conference on Computer Vision, 4015-4026.

Kirillov A, Wu Y, He K, Girshick R.2020. Pointrend: Image segmentation as rendering. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 9799-9808.

Koger B, Deshpande A, Kerby J T, Graving J M, Costelloe B R, Couzin I D. 2023. Quantifying the movement, behaviour and environmental context of group‐living animals using drones and computer vision. Journal of Animal Ecology, 92, 1357-1371.

Lauer J, Zhou M, Ye S, Menegas W, Schneider S, Nath T, Rahman M M, Di Santo V, Soberanes D, Feng G. 2022. Multi-animal pose estimation, identification and tracking with DeepLabCut. Nature Methods, 19, 496-504.

Leonard S M, Xin H, Ramirez B C, Stinn J P, Dutta S, Liu K, Brown-Brandl T M. 2021. Static and Dynamic Space Usage of Late-Gestation Sows. Transactions of the ASABE, 64, 151-159.

Li J, Green-Miller A R, Hu X, Lucic A, Mohan M M, Dilger R N, Condotta I C, Aldridge B, Hart J M, Ahuja N. 2022a. Barriers to computer vision applications in pig production facilities. Computers and electronics in agriculture, 200, 107227.

Li Y, Chen Y, Wang N, Zhang Z.2019. Scale-aware trident networks for object detection. Proceedings of the IEEE/CVF international conference on computer vision, 6054-6063.

Li Y, Wu C-Y, Fan H, Mangalam K, Xiong B, Malik J, Feichtenhofer C.2022b. Mvitv2: Improved multiscale vision transformers for classification and detection. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 4804-4814.

Lin T-Y, Goyal P, Girshick R, He K, Dollár P.2017. Focal loss for dense object detection. Proceedings of the IEEE international conference on computer vision, 2980-2988.

Liu S, Zeng Z, Ren T, Li F, Zhang H, Yang J, Li C, Yang J, Su H, Zhu J. 2023. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499,

Massa F, Girshick R.2018. maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

Matthews S G, Miller A L, PlÖtz T, Kyriazakis I. 2017. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Scientific reports, 7, 17582.

Melfsen A, Lepsien A, Bosselmann J, Koschmider A, Hartung E. 2023. Describing behavior sequences of fattening pigs using process mining on video data and automated pig behavior recognition. Agriculture, 13, 1639.

Nazarkevych M, Kostiak M, Oleksiv N, Vysotska V, Shvahuliak A-T.2024. A YOLO-based Method for Object Contour Detection and Recognition in Video Sequences. CPITS, 49-58.

Neethirajan S, Kemp B. 2021. Digital livestock farming. Sensing and Bio-Sensing Research, 32, 100408.

Norton T, Chen C, Larsen M L V, Berckmans D. 2019. Precision livestock farming: Building ‘digital representations’ to bring the animals closer to the farmer. Animal, 13, 3009-3017.

Pan Y, Zhang Y, Wang X, Gao X X, Hou Z. 2023. Low-cost livestock sorting information management system based on deep learning. Artificial Intelligence in Agriculture, 9, 110-126.

Qiao Y, Kong H, Clark C, Lomax S, Su D, Eiffert S, Sukkarieh S. 2021. Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation. Computers and electronics in agriculture, 185, 106143.

Radford A, Kim J W, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J.2021. Learning transferable visual models from natural language supervision. International conference on machine learning, 8748-8763.

Riekert M, Klein A, Adrion F, Hoffmann C, Gallmann E. 2020. Automatically detecting pig position and posture by 2D camera imaging and deep learning. Computers and electronics in agriculture, 174, 105391.

Schmidt T B, Lancaster J M, Psota E, Mote B E, Hulbert L E, Holliday A, Woiwode R, Pérez L C. 2022. Evaluation of a novel computer vision-based livestock monitoring system to identify and track specific behaviors of individual nursery pigs within a group-housed environment. Translational Animal Science, 6, txac082.

Shirke A, Golden R, Gautam M, Green-Miller A, Caesar M, Dilger R N. 2021. Vision-based behavioral recognition of novelty preference in pigs. arXiv preprint arXiv:2106.12181,

Siegford J M, Steibel J P, Han J, Benjamin M, Brown-Brandl T, Dórea J R, Morris D, Norton T, Psota E, Rosa G J. 2023. The quest to develop automated systems for monitoring animal behavior. Applied Animal Behaviour Science, 265, 106000.

T. Psota E, Schmidt T, Mote B, C. Pérez L. 2020. Long-term tracking of group-housed livestock using keypoint detection and map estimation for individual animal identification. Sensors, 20, 3670.

Tian M, Guo H, Chen H, Wang Q, Long C, Ma Y. 2019. Automated pig counting using deep learning. Computers and electronics in agriculture, 163, 104840.

Wang C-Y, Bochkovskiy A, Liao H-Y M.2023a. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 7464-7475.

Wang M, Li X, Larsen M L, Liu D, Rault J-L, Norton T. 2023b. A computer vision-based approach for respiration rate monitoring of group housed pigs. Computers and electronics in agriculture, 210, 107899.

Wu D, Cui D, Zhou M, Ying Y. 2022. Information perception in modern poultry farming: A review. Computers and electronics in agriculture, 199, 107131.

Yang Q, Xiao D. 2020. A review of video-based pig behavior recognition. Applied Animal Behaviour Science, 233, 105146.