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Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 39

Towards understanding animal welfare by observing collective flock behaviors via AI-powered Analytics

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DOI: http://dx.doi.org/10.15439/2024F2064

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 643–648 ()

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Abstract. Animal farming has undergone significant transformation and evolved from small-scale businesses to largescale commercial ventures. While maximizing productivity and profitability has always been a major concern in animal farming, during recent years there has been an increasing rise of concern regarding the welfare of the animals. In this context, the integration of artificial intelligence (AI) technologies offers immense potential for monitoring the well-being of chickens on farms and optimizing revenue streams simultaneously. Several works have integrated AI methodologies into everyday animal farming activities. Still, very few (if any) have proposed efficient and practical solutions that may facilitate farm owners in making impactful decisions regarding their business profitability and the welfare of the animals. In this direction, we propose a noninvasive chicken farm monitoring system that relies on onfield sound and video recordings integrated with sensory data acquired from the farm. The system consists of hardware that handles data acquisition and storage, a sensory data collection system and audio/video processing AI models. The last component of the system will be an inference engine that analyzes the collected data and infers useful facts about the flock's welfare and even psychological state.

References

  1. Haitham G. Abo-Al-Ela, Seham El-Kassas, Karima El-Naggar, Safaa E. Abdo, Ali Raza Jahejo, and Rasha A. Al Wakeel. “Stress and immunity in poultry: light management and nanotechnology as effective immune enhancers to fight stress”. In: Cell Stress and Chaperones 26.3 (2021), pp. 457–472. ISSN: 1355-8145. http://dx.doi.org/10.1007/s12192-021-01204-6. URL: https://www.sciencedirect.com/science/article/pii/S1355814523005187.
  2. Guillaume Alain and Yoshua Bengio. “What regularized auto-encoders learn from the data-generating distribution”. In: Journal of Machine Learning Research 15.1 (2014), pp. 3563–3593. http://dx.doi.org/10.5555/2627435.2750359. URL: https://dl.acm.org/doi/10.5555/2627435.2750359.
  3. Stevan Cakic, Tomo Popovic, Srdjan Krco, Daliborka Nedic, Dejan Babic, and Ivan Jovovic. “Developing Edge AI Computer Vision for Smart Poultry Farms Using Deep Learning and HPC”. In: Sensors 23.6 (2023). ISSN : 1424-8220. http://dx.doi.org/10.3390/s23063002. URL: https://www.mdpi.com/1424-8220/23/6/3002.
  4. Zhang Feiyang, Hu Yueming, Chen Liancheng, Guo Lihong, Duan Wenjie, and Wang Lu. “Monitoring behavior of poultry based on RFID radio frequency network”. In: International Journal of Agricultural and Biological Engineering 9.6 (2016), pp. 139–147.
  5. Colles Frances et al. “Monitoring chicken flock behaviour provides early warning of infection by human pathogen”. In: Royal Society B: Biological Sciences 283 (1822 2016).
  6. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Deep Residual Learning for Image Recognition”. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. IEEE Computer Society, 2016, pp. 770–778. http://dx.doi.org/10.1109/CVPR.2016.90. URL: https://doi.org/10.1109/CVPR.2016.90.
  7. Lihua Li, Yang Zhao, Jofran Oliveira, Wilco Verhoijsen, Kai Liu, and Hongwei Xin. “A UHF RFID system for studying individual feeding and nesting behaviors of group-housed laying hens”. In: Transactions of the ASABE 60.4 (2017), pp. 1337–1347.
  8. Stuart P. Lloyd. “Least squares quantization in PCM”. In: IEEE Trans. Inf. Theory 28.2 (1982), pp. 129–136. DOI : 10.1109/TIT.1982.1056489. URL : https://doi.org/10.1109/TIT.1982.1056489.
  9. Dawkins M.S., Roberts S.J., Cain R.J., Nickson T., and Donnelly C. A. “Early warning of footpad dermatitis and hockburn in broiler chicken flocks using optical flow, bodyweight and water consumption”. In: The Veterinary record of the British Veterinary Association 180.20 (2017), p. 499.
  10. Gerhard Manteuffel, Birger Puppe, and Peter C Schön. “Vocalization of farm animals as a measure of welfare”. In: Applied Animal Behaviour Science 88.1 (2004), pp. 163–182. ISSN: 0168-1591. http://dx.doi.org/10.1016/j.applanim.2004.02.012. URL: https://www.sciencedirect.com/science/article/pii/S0168159104000565.
  11. Sadia Naseem and Annie J King. “Ammonia production in poultry houses can affect health of humans, birds, and the environment-techniques for its reduction during poultry production”. In: Environmental science and pollution research international 25.16 (June 2018), pp. 15269–15293. ISSN : 0944-1344. http://dx.doi.org/10.1007/s11356-018-2018-y.
  12. Suresh Neethirajan. “ChickTrack – A quantitative tracking tool for measuring chicken activity”. In: Measurement 191 (2022), p. 110819. ISSN: 0263-2241. DOI: https://doi.org/10.1016/j.measurement.2022.110819. URL : https://www.sciencedirect.com/science/article/pii/S0263224122001154.
  13. Raspberry Pi. Raspberry Pi hardware. https://www.raspberrypi.com/. Accessed: 7th May, 2024. 2024.
  14. Lawrence Rabiner and Ronald Schafer. Theory and Applications of Digital Speech Processing. 1st. USA: Prentice Hall Press, 2010. ISBN : 0136034284.
  15. Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. “Faster R-CNN: Towards Real-Time Object Detection with Region ProposalNetworks”. In: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada. Ed. by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett. 2015, pp. 91–99. URL : https://proceedings.neurips.cc/paper/2015/hash/14bfa6bb14875e45bba028a21ed38046-Abstract.html.
  16. Chris Stauffer and W. Eric L. Grimson. “Adaptive Background Mixture Models for Real-Time Tracking”. In: 1999 Conference on Computer Vision and Pattern Recognition (CVPR ’99), 23-25 June 1999, Ft. Collins, CO, USA. IEEE Computer Society, 1999, pp. 2246–2252. DOI : 10.1109/CVPR.1999.784637. URL: https://doi.org/10.1109/CVPR.1999.784637. Ultralytics. ultralytics/YOLOv5. https://github.com/ultralytics/yolov5. Accessed: 7th May, 2024. 2024.
  17. Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. “Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion”. In: J. Mach. Learn. Res. 11 (2010), pp. 3371–3408. http://dx.doi.org/10.5555/1756006.1953039. URL: https://dl.acm.org/doi/10.5555/1756006.1953039.
  18. Xiao Yang, Lilong Chai, Ramesh Bahadur Bist, Sachin Subedi, and Zihao Wu. “A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor”. In: Animals 12.15 (2022). ISSN : 2076-2615. DOI : 10.3390/ani12151983. URL: https://www.mdpi.com/2076-2615/12/15/1983.
  19. Zoran Zivkovic. “Improved Adaptive Gaussian Mixture Model for Background Subtraction”. In: 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK, August 23-26, 2004. IEEE Computer Society, 2004, pp. 28–31. http://dx.doi.org/10.1109/ICPR.2004.1333992.