Loading...

​Behavior Recognition of Group-ranched Cattle from Video Sequences using Deep Learning

DOI: 10.18805/IJAR.B-1369    | Article Id: B-1369 | Page : 505-512
Citation :- ​Behavior Recognition of Group-ranched Cattle from Video Sequences using Deep Learning.Indian Journal of Animal Research.2022.(56):505-512
Rotimi-Williams Bello, Ahmad Sufril Azlan Mohamed, Abdullah Zawawi Talib, Salisu Sani, Mohd Nadhir Ab Wahab sirbrw@yahoo.com
Address : School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia.
Submitted Date : 1-04-2021
Accepted Date : 30-07-2021

Abstract

Background: One important indicator for the wellbeing status of livestock is their daily behavior. More often than not, daily behavior recognition involves detecting the heads or body gestures of the livestock using conventional methods or tools. To prevail over such limitations, an effective approach using deep learning is proposed in this study for cattle behavior recognition.
Methods: The approach for detecting the behavior of individual cows was designed in terms of their eating, drinking, active, and inactive behaviors captured from video sequences and based on the investigation of the attributes and practicality of the state-of-the-art deep learning methods.
Result: Among the four models employed, Mask R-CNN achieved average recognition accuracies of 93.34%, 88.03%, 93.51% and 93.38% for eating, drinking, active and inactive behaviors. This implied that Mask R-CNN achieved higher cow detection accuracy and speed than the remaining models with 20 fps, making the proposed approach competes favorably well with other approaches and suitable for behavior recognition of group-ranched cattle in real-time.

Keywords

​Behavior recognition Deep learning Group-ranched cattle Mask R-CNN

References

  1. Bello, R., Talıb, A. and Mohamed, A. (2020a). Deep learning-based architectures for recognition of cow using cow nose image pattern. Gazi University Journal of Science. 33: 831-44. DOI: https://doi.org/10.35378/gujs.605631.
  2. Bello, R.W., Olubummo, D.A., Seiyaboh, Z., Enuma, O.C., Talib, A.Z. and Mohamed, A.S.A. (2020b). Cattle identification: the history of nose prints approach in brief. In IOP Conference Series: Earth and Environmental Science. 594: 1-9. DOI: 10.1088/1755-1315/594/1/012026.
  3. Bello, R.W., Talib, A.Z. and Mohamed, A.S.A. (2021a). Contour extraction of individual cattle from an image using enhanced Mask R-CNN instance segmentation method. IEEE Access. 9: 56984-57000. DOI: 10.1109/ACCESS.2021. 3072636.
  4. Bello, R.W., Talib, A.Z.H. and Mohamed, A.S.A.B. (2021b). Deep belief network approach for recognition of cow using cow nose image pattern. Walailak Journal of Science and Technology. 18: 1-14. DOI: https://doi.org/10.48048/wjst. 2021.8984.
  5. Bochkovskiy, A., Wang, C.Y. and Liao, H.Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. 1-17.
  6. Chauhan, J.H., Hadiya, K.K., Dhami, A.J. and Sarvaiya, N.P. (2021). Ovarian dynamics, plasma endocrine profile and fertility response following synchronization protocols in crossbred cows with cystic ovaries. Indian Journal of Animal Research. 55: 127-133. DOI: 10.18805/ijar.B-3944.
  7. Chouhan, D., Aich, R., Jain, R.K. and Chhabra, D. (2021). Acute phase protein as biomarker for diagnosis of sub-clinical mastitis in cross-bred cows. Indian Journal of Animal Research. 55: 193-198. DOI: 10.18805/ijar.B-3943.
  8. Dohare, A.K., Bangar, Y.C., Sharma, V.B. and Verma, M.R. (2021). Modelling the effect of mastitis on milk yield in dairy cows using covariance structures fitted to repeated measures. Indian Journal of Animal Research. 55: 11-14. DOI: 10.18805/ijar.B-3919.
  9. Fuentes, A., Yoon, S., Park, J. and Park, D.S. (2020). Deep learning- based hierarchical cattle behavior recognition with spatio- temporal information. Computers and Electronics in Agriculture. 177: 1-11. DOI: https://doi.org/10.1016/j.compag. 2020. 105627.
  10. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2020). Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42: 386-397. DOI: 10.1109/TPAMI.2018. 2844175.
  11. He, K., Gkioxari, G., Dollár, P. and Girshick, R. (2017). Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision. 2961-2969. DOI: 10.1109/ICCV.2017.322.
  12. Jiang, B., Wu, Q., Yin, X., Wu, D., Song, H. and He, D. (2019). FLYOLOv3 deep learning for key parts of dairy cow body detection. Computers and Electronics in Agriculture. 166: 1-8. DOI: https://doi.org/10.1016/j.compag.2019.104982.
  13. Jiang, M., Rao, Y., Zhang, J. and Shen, Y. (2020). Automatic behavior recognition of group housed goats using deep learning. Computers and Electronics in Agriculture. 177: 1-13. DOI: https://doi.org/10.1016/j.compag.2020.105706.
  14. Jingqiu, G., Zhihai, W., Ronghua, G. and Huarui, W. (2017). Cow behavior recognition based on image analysis and activities. International Journal of Agricultural and Biological Engineering.10: 165-174. DOI: 10.3965/j.ijabe. 2017 1003.3080.
  15. Kashiha, M.A., Bahr, C., Ott, S., Moons, C.P.H., Niewold, T.A., Tuyttens, F. and Berckmans, D. (2014). Automatic monitoring of pig locomotion using image analysis. Livestock Science. 159: 141-148. https://doi.org/10.1016/j.livsci.2013.11. 007.
  16. 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: 1-19. DOI: https://doi.org/10.3390/s17122757.
  17. Lao, F., Brown-Brandl, T., Stinn, J.P., Liu, K., Teng, G. and Xin, H. (2016). Automatic recognition of lactating sow behaviors through depth image processing. Computers and Electronics in Agriculture. 125: 56-62. DOI: https://doi.org/10.1016/ j.compag.2016.04.026.
  18. Nasirahmadi, A., Hensel, O., Edwards, S.A. and Sturm, B. (2017). A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method. Animal. 11: 131-139.  DOI: 10.1017/S1751731116001208.
  19. Pedersen, L.J. (2018). Overview of commercial pig production systems and their main welfare challenges, Advances in Pig Welfare. 1-23. DOI: https://doi.org/10.1016/B978-0- 08-101012 9.00001-0.
  20. Redmon, J. and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv Prepr. arXiv 1804.02767, 1-6.
  21. Ren, S., He, K., Girshick, R. and Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39: 1137-1149. DOI: 10.1109/TPAMI. 2016.2577031.
  22. Russell, B.C., Torralba, A., Murphy, K.P. and Freeman, W.T. (2008). LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision. 77: 157-173. DOI: https://doi.org/10.1007/s11263-007-0090-8.
  23. Saberioon, M.M. and Cisar, P. (2016). Automated multiple fish tracking in three-dimension using a structured light sensor. Computers and Electronics in Agriculture. 121: 215-221. DOI: https:/ /doi.org/10.1016/j.compag. 2015.12.014.
  24. Shen, W., Cheng, F., Zhang, Y., Wei, X., Fu, Q. and Zhang, Y. (2020). Automatic recognition of ingestive-related behaviors of dairy cows based on triaxial acceleration. Information Processing in Agriculture. 7: 427-443. DOI: https://doi.org/ 10.1016/j.inpa.2019.10.004.
  25. Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv Prepr. arXiv 1409.1556. 1-14.
  26. Stavrakakis, S., Li, W., Guy, J.H., Morgan, G., Ushaw, G., Johnson, G.R. and Edwards, S.A. (2015). Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs. Computers and Electronics in Agriculture. 117: 1-7.  DOI: https://doi.org/10.1016/j.compag.2015.07.003.
  27. Thorat, A.B., Borikar, S.T., Siddiqui, M.F.M.F., Rajurkar, S.R., Moregaonkar, S.D., Ghorpade, P.B., and Khawale, T.S. (2021). A study on occurrence and haemato-biochemical alterations in SARA in cattle treated with different therapeutic regimens. Indian Journal of Animal Research. 55: 90-95. DOI: 10.18805/IJAR.B-3929.
  28. Velarde, A., Fàbrega, E., Blanco-Penedo, I. and Dalmau, A. (2015). Animal welfare towards sustainability in pork meat production. Meat Science. 109: 13-17. DOI: https:// doi.org/10.1016/j.meatsci.2015.05.010.
  29. Yang, A., Huang, H., Zheng, B., Li, S., Gan, H., Chen, C., Yang, X. and Xue, Y. (2020). An automatic recognition framework for sow daily behaviours based on motion and image analyses. Biosystems Engineering. 192: 56-71. DOI: https:// doi.org/10.1016/j.biosystemseng.2020.01.016.
  30. Yang, Q., Xiao, D. and Lin, S. (2018b). Feeding behavior recognition for group-housed pigs with the Faster R-CNN. Computers and Electronics in Agriculture. 155: 453-460. DOI: https:/ /doi.org/10.1016/j.compag.2018.11.002.
  31. Zaborski, D. and Grzesiak, W. (2021). Utilization of boosted classification trees for the detection of cows with conception difficulties. Indian Journal of Animal Research. 55: 359- 363. DOI: 10.18805/ijar.B-1103.
  32. Zheng, C., Zhu, X., Yang, X., Wang, L., Tu, S. and Xue, Y. (2018). Automatic recognition of lactating sow postures from depth images by deep learning detector. Computers and Electronics in Agriculture. 147: 51-63. DOI: https://doi.org/ 10.1016/j.compag.2018.01.023.
  33. Zhu, W., Guo, Y., Jiao, P., Ma, C. and Chen, C. (2017). Recognition and drinking behaviour analysis of individual pigs based on machine vision. Livestock Science. 205: 129-136. DOI: https://doi.org/10.1016/j.livsci.2017.09.003.

Global Footprints