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All issues Volume 448 (2023) E3S Web Conf., 448 (2023) 02064 References
Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 02064
Number of page(s) 13
Section Information System
DOI https://doi.org/10.1051/e3sconf/202344802064
Published online 17 November 2023
  1. E. U. Chowdhury, A. Morey, Intelligent Packaging for Poultry Industry, Journal of Applied Poultry Research, Elsevier Inc., 28, 4, 791–800, Des 01 (2019) [CrossRef] [Google Scholar]
  2. C. M. Wathes, H. H. Kristensen, J. M. Aerts, D. Berckmans, Is precision livestock farming an engineer’s daydream or nightmare, an animal’s friend or foe, and a farmer’s panacea or pitfall?, Comput. Electron. Agric., 64, 1, 2–10 (2008) [CrossRef] [Google Scholar]
  3. L. Li, Y. Zhao, J. Oliveira, K. Liu, A UHF RFID System for Studying Individual Feeding and Nesting Behaviors of Group-Housed Laying Hens A UHF RFID System for Studying Individual Feeding and Nesting (2017) [Google Scholar]
  4. D. Berckmans, General introduction to precision livestock farming, 6–11 (2017) [Google Scholar]
  5. J. Huang, W. Wang, T. Zhang, Method for detecting avian influenza disease of chickens based on sound analysis, Biosyst. Eng., 180, 16–24 (Apr, 2019) [CrossRef] [Google Scholar]
  6. C. Fang, J. Huang, K. Cuan, X. Zhuang, T. Zhang, Comparative study on poultry target tracking algorithms based on a deep regression network, Biosyst. Eng., 190, 176–183 (Feb, 2020) [CrossRef] [Google Scholar]
  7. X. Zhuang, T. Zhang, Detection of sick broilers by digital image processing and deep learning, Biosyst. Eng., 179, 106–116 (Mar, 2019) [CrossRef] [Google Scholar]
  8. N. Li, Z. Ren, D. Li, L. Zeng, Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming, Animal, 14, 3. Elsevier B.V., 617–625 (Jan 01, 2020) [CrossRef] [Google Scholar]
  9. M. S. Dawkins, S. J. Roberts, R. J. Cain, T. Nickson, C. A. Donnelly, Paper Early warning of footpad dermatitis and hockburn in broiler chicken fl ocks using optical fl ow , bodyweight and water consumption (2017) [Google Scholar]
  10. C. Okinda, A review on computer vision systems in monitoring of poultry: A welfare perspective, Artif. Intell. Agric., 4, 184–208 (2020) [Google Scholar]
  11. C. Fang, T. Zhang, H. Zheng, J. Huang, dan K. Cuan, Pose estimation and behavior classification of broiler chickens based on deep neural networks, Comput. Electron. Agric., 180 (Jan, 2021) [Google Scholar]
  12. C. Okinda, A machine vision system for early detection and prediction of sick birds: A broiler chicken model, Biosyst. Eng., 188, 229–242 (Des, 2019) [CrossRef] [Google Scholar]
  13. N. S. N. Abd Aziz, S. Mohd Daud, R. A. Dziyauddin, M. Z. Adam, A. Azizan, A Review on Computer Vision Technology for Monitoring Poultry Farm - Application, Hardware, and Software, IEEE Access, Institute of Electrical and Electronics Engineers Inc., 9, 12431–12445 (2021) [Google Scholar]
  14. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Trans. Pattern Anal. Mach. Intell., 39, 6, 1137–1149 (2017) [CrossRef] [PubMed] [Google Scholar]
  15. A. Fuentes, S. Yoon, S. C. Kim, D. S. Park, A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition, Sensors (Switzerland), 17, 9 (2017) [Google Scholar]
  16. G. S. Cheema S. Anand, Automatic Detection and Recognition of Individuals in Patterned Species, 1, 228–240 (2017) [Google Scholar]
  17. F. Guo, Y. Qian, Y. Shi, Real-time railroad track components inspection based on the improved YOLOv4 framework, Autom. Constr., 125, February, 103596 (2021) [CrossRef] [Google Scholar]
  18. S. Neethirajan, The role of sensors, big data and machine learning in modern animal farming, Sensing and Bio-Sensing Research, 29. Elsevier B.V. (Agu 01, 2020) [Google Scholar]
  19. I. Choi, J. Kim, J. Jang, Development of marker-free night-vision displacement sensor system by using image convex hull optimization, Sensors (Switzerland), 18, 12 (2018) [Google Scholar]
  20. G. Li, Practices and applications of convolutional neural network-based computer vision systems in animal farming: A review, Sensors, 21, 4, 1–42 (2021) [Google Scholar]
  21. M. Elgendy, Deep Learning for Vision Systems (2020) [Google Scholar]
  22. H. Purwins, B. Li, T. Virtanen, J. Schlüter, S. Chang, T. Sainath, Deep Learning for Audio Signal Processing, IEEE J. Sel. Top. Signal Process., 13, 2, 206–219 (2019) [CrossRef] [Google Scholar]
  23. S. López-Tapia, R. Molina, A. K. Katsaggelos, Deep learning approaches to inverse problems in imaging: Past, present and future, Digit. Signal Process., 1, 1 103285 (2021) [CrossRef] [Google Scholar]
  24. J. Díaz-Ramírez, Machine Learning and Deep Learning, Ingeniare, 29, 2, 182–183 (2021) [Google Scholar]
  25. M. Momeny, A. M. Latif, M. Agha Sarram, R. Sheikhpour, Y. D. Zhang, A noise robust convolutional neural network for image classification, Results Eng., 10, February, 100225 (2021) [CrossRef] [Google Scholar]
  26. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You Only Look Once: Unified, Real-Time Object Detection. [Google Scholar]

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