Assessment of Deep Learning Models for Poultry Disease Detection and Diagnostics: A Survey Paper

Augustine Mukumba; Melford Mutandavari1

1

Publication Date: 2024/07/26

Abstract: This study focuses on the assessment of a deep learning model for the detection and diagnostics of poultry diseases. The model utilizes a convolutional neural network architecture to automatically analyze images of diseased poultry and accurately classify the type of disease present. The performance of the model is evaluated by comparing its predictions with expert- annotated data. The results show that the deep learning model achieves high accuracy in detecting common poultry diseases, outperforming traditional methods. This novel approach has the potential to revolutionize the field of poultry healthcare by providing fast and accurate diagnostics, leading to improved disease management and welfare for poultry populations.

Keywords: Convolutional Neural Networks, Poultry Disease, Deep Learning, Detection, Diagnostics and Classification.

DOI: https://doi.org/10.38124/ijisrt/IJISRT24JUL463

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JUL463.pdf

REFERENCES

  1. J. Zhu and M. Zhou, “Online detection of abnormal chicken manure based on machine vision,” in 2021 ASABE Annual International Virtual Meeting, July 12-16, 2021, 2021, vol. 1, pp. 595–601, doi: 10.13031/ aim.202100188.
  2. C. Wang, E. Benetos, S. Wang, and E. Versace, “Joint scattering for automatic chick call recognition,” in European Signal Processing Conference, Aug. 2022, vol. 2022-Augus, pp. 195–199, doi: 10.23919/eusipco 55093.2022.9909738.
  3. L. Carpentier, E. Vranken, D. Berckmans, J. Paeshuyse, and T. Norton, “Development of sound-based poultry health monitoring tool for automated sneeze detection,” Computers and Electronics in Agriculture, vol. 162, pp. 573–581, Jul. 2019, doi: 10. 1016/j.compag.2019.05.013.
  4. J. Huang, W. Wang, and T. Zhang, “Method for detecting avian influenza disease of chickens based on sound analysis,” Biosystems Engineering, vol. 180, pp. 16–24, Apr. 2019, doi: 10.1016/j.biosystemseng. 2019.01.015.
  5. H. O. Aworinde et al., “Poultry fecal imagery dataset for health status prediction: a case of South-West Nigeria,” Data in Brief, vol. 50, p. 109517, Oct. 2023, doi: 10.1016/j.dib.2023.109517.
  6. H. Mbelwa, J. Mbelwa, and D. Machuve, “Deep convolutional neural network for chicken diseases detection,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 2, pp. 759–765, 2021, doi: 10.14569/IJACSA.2021.0120295.
  7. S. Suthagar, G. Mageshkumar, M. Ayyadurai, C. Snegha, M. Sureka, and S. Velmurugan, “Faecal image-based chicken disease classification using deep learning techniques,” Lecture Notes in Networks and Systems, vol. 563, pp. 903–917, 2023, doi: 10.1007/ 978-981-19-7402-1_64.
  8. W. Widyawati and W. Gunawan, “Early detection of sick chicken using artificial intelligence,” Teknika: Jurnal Sains dan Teknologi, vol. 18, no. 2, p. 136, Nov. 2022, doi: 10.36055/tjst.v18i2.17337.
  9. J. Bao and Q. Xie, “Artificial intelligence in animal farming: A systematic literature review,” Journal of Cleaner Production, vol. 331, p. 129956, Jan. 2022, doi: 10.1016/j.jclepro.2021.129956.
  10. L.-D. Quach, N. Pham-Quoc, D. C. Tran, and M. F. Hassan, “Identification of chicken diseases using VGGNet and ResNet models,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 334, 2020, pp. 259–269.
  11. A. H. Bin Hilmi, “Poultry pose estimation by detecting keypoints with deep convolutional network,” College of Engineering Universiti Tenaga Nasional, 2022.
  12. C. Fang, H. Zheng, J. Yang, H. Deng, and T. Zhang, “Study on poultry pose estimation based on multi-parts detection,” Animals, vol. 12, no. 10, p. 1322, May 2022, doi: 10.3390/ani12101322.
  13. C. Fang, T. Zhang, H. Zheng, J. Huang, and K. Cuan, “Pose estimation and behavior classification of broiler chickens based on deep neural networks,” Computers and Electronics in Agriculture, vol. 180, p. 105863, Jan. 2021, doi: 10.1016/j.compag.2020.105863.
  14. A. Nasiri, J. Yoder, Y. Zhao, S. Hawkins, M. Prado, and H. Gan, “Pose estimation-based lameness recognition in broiler using CNN-LSTM network,” Computers and Electronics in Agriculture, vol. 197, p. 106931, Jun. 2022, doi: 10.1016/j.compag.2022. 106931.
  15. B. X. Xie and C. L. Chang, “Behavior recognition of a broiler chicken using long short-term memory with convolution neural networks,” in 2022 International Automatic Control Conference, CACS 2022, Nov. 2022, pp. 1–5, doi: 10.1109/CACS55319.2022. 9969848.
  16. Wang J T, Shen M X, Liu L S, Xu Y, Okinda C. Recognition and classification of broiler droppings based on deep convolutional neural network. Journal of Sensors, 2019; 2019: 3823515. doi: 10.1155/2019/ 3823515.