References
Adebayo, S., Halleluyah, O., Aworine, A., Akinwunmi, O., & Echentama, K. (2023). Enhancing poultry health management through machine learning-based analysis of vocalization signal dataset. Data in Brief. https://doi.org/10.1016/j.dib.2023.109528 Ahmed, G., Rauf, A. S. M., Sumaiyah, Z., & Abdullah, G. (2021). An approach towards IoT-based predictive service for early detection of diseases in poultry chickens. Sustainability, 13(23), 13396. https://doi.org/10.3390/su132313396 Ajay, G. B., Deepika, A., & Aniruddha, S. R. (2024). Mathematical formulation of deep learning model for poultry disease classification using EfficientNet-B3 CNN model. Communications on Applied Nonlinear Analysis, 31(2s). Akshaya, V. B. M., Vallabhee, S., Baig, M. A., & Kumar, G. B. C. (2024). Advancements in poultry disease detection: A comprehensive review of deep learning methods and emerging trends. Indiana Journal of Multidisciplinary Research, 4(3). https://doi.org/10.5281/zenodo.12685689 Al-Momen, R., Sonjoy, D., Wasi, U. B., Sahadat, I. E., Nusrat, J., & Raiyan, R. (2024). An ensemble deep learning approach to detect common chicken diseases from fecal matter images. Proceedings of the ACM, 1-10. https://doi.org/10.1145/3723178.3723198 AnifM, A. A. B., Pin Jern, K., Shirley, G. H. T., Fatin, N. A., Indra, M., Baharuddin, M. Z., & Omar, A. R. (2023). A hybrid chromaticity-morphological machine learning model to detect Newcastle disease in chicken within 36 hours. SSRN.https://ssrn.com/abstract=4805571 Banakar, A., Mohammad, S., & Abdolhamid, S. (2016). An intelligent device for diagnosing avian diseases: Newcastle, infectious bronchitis, avian influenza. Computers and Electronics in Agriculture, 127, 744-753. https://doi.org/10.1016/j.compag.2016.08.006 Chen, Y., & Yang, H. (2023). Chicken manure disease recognition model based on improved ResNet50. Journal of Animal Health Research. Dwicahyo, A., Ilham, M. N., Taufik, A., & Ubaid, D. (2024). Early detection of disease in chicks using CNN on Bangkok chicken health. Bulletin of Information Technology, 6(2), 126-141. https://doi.org/10.12928/biste.v6i2.10245 Emmanuel, A. I., Osaghae, E. O., & Frederick, D. B. (2023). Deep learning implementation for poultry disease detection and control. International Journal of Innovative Science and Research Technology, 8(6). Esraa, H., Samar, E., Mahmoud, Y. S., & Nora, E. (2024). Optimizing poultry audio signal classification with deep learning and bottleneck layer fusion. Journal of Big Data.https://doi.org/10.1186/s40537-024-00985-8 Fang, C., Junduan, H., Kaixuan, C., Xiaolin, Z., & Tiemin, Z. (2020). Comparative study on poultry target tracking algorithms based on a deep regression network. Biosystems Engineering, 190, 12-24. https://doi.org/10.1016/j.biosystemseng.2019.12.002 Jakovljevic, N., Maljkovic, N., Miskovic, D., Knezevic, P., & Delic, V. (2019). A broiler stress detection system based on audio signal processing. Computers and Electronics in Agriculture. Kalaiselvi, T. C., Dinesh, A., Arjun, K., & Bharath, S. (2023). Detection and classification of disease in poultry farm. Journal of Survey in Fisheries Sciences. Kumar, C. B., & Punitha, R. (2020). YOLOv3 and YOLOv4: Multiple object detection for surveillance applications. Proceedings of ICSSIT. https://doi.org/10.1109/ICSSIT48917.2020.9214094 Li, Z., Zhang, T., Cuan, K., Fang, C., Zhao, H., Guan, C., Yang, Q., & Qu, H. (2022). Sex detection of chicks based on audio technology and deep learning methods. Animals, 12(22), 3106. https://doi.org/10.3390/ani12223106 Likitha, R., Harshitha, M. P. B., & Rashmi, M. (2024). Classification and detection of chicken disease using CNN with image classification technique. International Journal of Engineering Research and Applications, 14(6), 126-129. Machuve, D., Ezinne, N., Neema, M., & Jimmy, M. (2022). Poultry disease diagnostics models using deep learning. Frontiers in Artificial Intelligence, 5, 733345. https://doi.org/10.3389/frai.2022.733345 Mbelwa, H., Jimmy, T. M., & Dina, M. (2021). Deep convolutional neural network for chicken diseases detection. International Journal of Advanced Computer Science and Applications, 12(2). https://doi.org/10.14569/IJACSA.2021.0120295 Mbelwa, H., Ezinne N., & Neema D. (2022).Poultry diseases diagnostics models using deep learning. Volume 5 - 2022. https://doi.org/10.3389/frai.2022.733345 Mukumba, A., & Melford, M. (2024). Assessment of deep learning models for poultry disease detection and diagnostics: A survey. International Journal of Innovative Science and Research Technology, 9(7). https://doi.org/10.38124/ijisrt/IJISRT24JUL463 Musa Y. M, Sani D. A. A., Simon S/ W., and Lydia C.O.(2024). Hybrid Diagnostic System for Groundnut Diseases.International Journal of Current Researchesin Sciences, Social Sciences and Language. Volume 04 Nakrosis, A., Agne, P. T., Vidas, R., & Ingrida, L. B. (2023). Towards early poultry health prediction through non-invasive and computer vision-based dropping classification. Animals, 13(19), 3041. https://doi.org/10.3390/ani13193041 Quach, L. D., Nghi, Q. P., Tran Duc, C., & Mohd, F. H. (2020). Identification of chicken diseases using VGGNet and ResNet models. Conference Paper. Rashed, O., Ajayi, A., & Akanbi, L. A. (2024). Internet of Things and machine learning techniques in poultry health and welfare management: A systematic literature review. Big Data, Enterprise and Artificial Intelligence Laboratory, University of West England. Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60. https://doi.org/10.1186/s40537-019-0197-0 Surya, S., Vedanarayanan, S. R., Hrithik, K. V., & Vishnu, M. (2022). Poultry management system with detection of sick broilers. International Journal of Engineering Technology and Management Sciences, 6(4). https://doi.org/10.46647/ijetms.2022.v06i04.0040 Vrindavanam, J., Pradeep, K., & Govind, P. (2023). Poultry disease identification in fecal images using vision transformer. Machine Learning in Applied Environmental Sciences, 6.https://doi.org/10.55162/MCAES.06.150 Wang, S., Lili, W., Jingfei, W., & Zhigang, Z. (2024). Epidemiological study of Newcastle disease in chicken farms in China, 2019-2022. Frontiers in Veterinary Science, 11, 1410878. https://doi.org/10.3389/fvets.2024.1410878 Xiaolin Z. a., Zhang T.(2019). Detection of sick broilers by digital imageprocessing and deep learning.www.elsevier.com/locate/issn/15375110 Yajie, L., Gapar, J. M., & Asif, I. H. (2023). Poultry disease early detection methods using deep learning technology. Indonesian Journal of Electrical Engineering and Computer Science, 32(3), 1712-1723. https://doi.org/10.11591/ijeecs.v32.i3.pp1712-1723 Zelalem, D., & Gizeaddis, S. L. (2023). Smartphone-based detection and classification of poultry diseases from chicken fecal images using deep learning techniques. ResearchGate Preprint. Zhang, N., Ma, X., Huang, Y., & Bai, J. (2023). Image recognition of chicken diseases based on improved residual networks. In Y. Li, Z. Huang, M. Sharma, L. Chen, & R. Zhou (Eds.), Health Information Science (Vol. 14305, pp. 222-232). Springer.