REFERENCES
1. Hu H, Dai B, Shen W, et al. Cow identification based on fusion of deep parts features. Biosystems Engineering 2020;192:245-56.
2. Kumar S, Pandey A, Sai Ram Satwik K, et al. Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement 2018;116:1-17.
3. Uenishi S, Oishi K, Kojima T, et al. A novel accelerometry approach combining information on classified behaviors and quantified physical activity for assessing health status of cattle: a preliminary study. Applied Animal Behaviour Science 2021;235:105220.
4. Niloofar P, Francis DP, Lazarova-molnar S, et al. Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: overview and challenges. Computers and Electronics in Agriculture 2021;190:106406.
5. Shen W, Cheng F, Zhang Y, Wei X, Fu Q, Zhang Y. Automatic recognition of ingestive-related behaviors of dairy cows based on triaxial acceleration. Information Processing in Agriculture 2020;7:427-43.
6. Benaissa S, Tuyttens FA, Plets D, et al. Classification of ingestive-related cow behaviors using RumiWatch halter and neck-mounted accelerometers. Applied Animal Behaviour Science 2019;211:9-16.
7. Barker ZE, Vázquez Diosdado JA, Codling EA, et al. Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle. J Dairy Sci 2018;101:6310-21.
8. Arablouei R, Currie L, Kusy B, Ingham A, Greenwood PL, Bishop-hurley G. In-situ classification of cattle behavior using accelerometry data. Computers and Electronics in Agriculture 2021;183:106045.
9. Benaissa S, Tuyttens F, Plets D, et al. Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors. Computers and Electronics in Agriculture 2020;168:105153.
10. Ren XH, Liu G, Zhang M, Si YS, Zhang XY, MA L. Dairy cattle's behaviour recognition method based on support vector machine classification model. Transactions of the Chinese Society for Agricultural 2019;50:290-6.
11. Wang K, Liu CH, Duan QL. Identification of sow oestrus behaviour based on MFO-LSTM. Transactions of the Chinese Society of Agricultural Engineering 2020;36:211-9.
12. Khin MP, Zin TT, Mar CC, Tin P, Horii Y. Cattle pose classification system using deeplabcut and svm model. 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE); 2022 Oct 494-5; Osaka, Japan.
13. Minar MR, Naher J. Recent advances in deep learning: an overview. Available from: https://arxiv.org/abs/1807.08169 [Last accessed on 25 Jan 2024].
14. Jiang B, Chen S, Wang B, Luo B. MGLNN: semi-supervised learning via multiple graph cooperative learning neural networks. Neural Netw 2022;153:204-14.
15. Zhang HM, Fu ZY, Han WT, Yang G, Niu DD, Zhou XY. Detection method of maize seedlings number based on improved YOLO. Transactions of the Chinese Society for Agricultural 2021;52:221-9.
16. Roy AM, Bhaduri J. DenseSPH-YOLOv5: An automated damage detection model based on densenet and swin-transformer prediction head-enabled YOLOv5 with attention mechanism. Advanced Engineering Informatics 2023;56:102007.
17. Hu Z, Yang H, Lou T. Dual attention-guided feature pyramid network for instance segmentation of group pigs. Computers and Electronics in Agriculture 2021;186:106140.
18. Xiao J, Liu G, Wang K, Si Y. Cow identification in free-stall barns based on an improved mask R-CNN and an SVM. Computers and Electronics in Agriculture 2022;194:106738.
19. Bonneau M, Vayssade J, Troupe W, Arquet R. Outdoor animal tracking combining neural network and time-lapse cameras. Computers and Electronics in Agriculture 2020;168:105150.
20. Su Q, Tang J, Zhai M, He D. An intelligent method for dairy goat tracking based on Siamese network. Computers and Electronics in Agriculture 2022;193:106636.
21. Chen C, Zhu W, Steibel J, et al. Recognition of aggressive episodes of pigs based on convolutional neural network and long short-term memory. Computers and Electronics in Agriculture 2020;169:105166.
22. Chen C, Zhu W, Steibel J, Siegford J, Han J, Norton T. Classification of drinking and drinker-playing in pigs by a video-based deep learning method. Biosystems Engineering 2020;196:1-14.
23. Chen C, Zhu W, Steibel J, Siegford J, Han J, Norton T. Recognition of feeding behaviour of pigs and determination of feeding time of each pig by a video-based deep learning method. Computers and Electronics in Agriculture 2020;176:105642.
24. Yan HW, Liu ZY, Fui QL, Hu ZW. Multi-target detection based on feature pyramid attention and deep convolution network for pigs. Transactions of the Chinese Society of Agricultural Engineering 2020;36:193-202.
25. Li X, Cai C, Zhang R, Ju L, He J. Deep cascaded convolutional models for cattle pose estimation. Computers and Electronics in Agriculture 2019;164:104885.
26. Wei S, Ramakrishna V, Kanade T, Sheikh Y. Convolutional pose machines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016 4724-32.
27. Newell A, Yang K, Deng J. Stacked hourglass networks for human pose estimation. In: Leibe B, Matas J, Sebe N, Welling M, editors. Computer Vision – ECCV 2016. Cham: Springer International Publishing; 2016. pp. 483-99.
28. Bulat A, Tzimiropoulos G. Human pose estimation via convolutional part heatmap regression. In: Leibe B, Matas J, Sebe N, Welling M, editors. Computer Vision – ECCV 2016. Cham: Springer International Publishing; 2016. pp. 717-32.
29. Wang SH, He DJ, Liu D. Automatic recognition method of dairy cow estrus behaviour based on machine visionAutomatic recognition method of dairy cow estrus behaviour based on machine vision. Transactions of the Chinese Society for Agricultural 2020;51:241-9.
30. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017;60:84-90.
31. Yin X, Wu D, Shang Y, Jiang B, Song H. Using an efficientnet-LSTM for the recognition of single cow’s motion behaviours in a complicated environment. Computers and Electronics in Agriculture 2020;177:105707.
32. Benaissa S, Tuyttens FAM, Plets D, et al. On the use of on-cow accelerometers for the classification of behaviours in dairy barns. Res Vet Sci 2019;125:425-33.
33. Wang SH, He DJ. Estrus behavior recognition of dairy cows based on improved YOLO v3 model. Transactions of the Chinese Society for Agricultural Machinery 2021;52:141-50.
34. Zhang HM, Li YH, Zhou LX, Wang R, Li SQ, Wang HY. Multi-target skeleton extraction method of beef cattle based on improved YOLO v3. Transactions of the Chinese Society for Agricultural Machinery 2022;53:285-93.
35. Ma S, Zhang Q, Li T, Song H. Basic motion behavior recognition of single dairy cow based on improved Rexnet 3D network. Computers and Electronics in Agriculture 2022;194:106772.
36. Ji JT, Liu QH, Gao RH, Li QF, Zhao KX, Bai Q. Ruminant behavior analysis method of dairy cows with improved flownet 2.0 optical flow algorithm. Transactions of the Chinese Society for Agricultural Machinery 2023;54:235-42.
37. Guo JJ, He GH, Xu LQ, Liu TL, Feng DC, Liu SY. Pigeon behavior detection model based on improved YOLO v4. Transactions of the Chinese Society for Agricultural Machinery 2023;54:347-55.
38. Hu T, Yan R, Jiang C, et al. Grazing sheep behaviour recognition based on improved YOLOV5. Sensors 2023;23:4752.
39. Bochkovskiy A, Wang CY, Liao HYM. YOLOv4: optimal speed and accuracy of object detection. ArXiv 2020; preprint arXiv:10934.
40. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016.p.779-788.
41. Gao SH, Cheng MM, Zhao K, Zhang XY, et al. Res2Net: a new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence; 2021.p.652-662.
42. Cao Y, Xu J, Lin S, et al. Gcnet: Non-local networks meet squeeze-excitation networks and beyond. Proceedings of the IEEE/CVF international conference on computer vision workshops; 2019.