Digital Transformation of Poultry Farming Through Artificial Intelligence
DOI:
https://doi.org/10.56127/ijst.v4i3.2297Keywords:
Artificial Intelligence, Poultry Farming, Precision Livestock Farming, Smart Agriculture, Digital TransformationAbstract
The global poultry sector is under pressure to increase efficiency, sustainability, and animal welfare amid growing demand and resource constraints. Artificial Intelligence (AI) has emerged as a key enabler of digital transformation in poultry farming, yet evidence on practical adoption remains fragmented, especially for smallholder and UMKM contexts. Objective: This study systematically maps AI applications in poultry farming, classifies their functional domains and technological approaches, evaluates reported benefits and limitations, and identifies research gaps related to real-world implementation. Method: A PRISMA 2020-guided Systematic Literature Review (SLR) was conducted on 28 peer-reviewed, open-access, Scopus-indexed journal articles published between 2020 and 2025. Data were extracted on AI techniques, data modalities, application domains, implementation settings, and reported outcomes, then synthesized using thematic analysis. Findings: AI applications concentrate on disease detection and health monitoring, environmental control, behavior and welfare analysis, feed optimization, and productivity forecasting. Deep learning and computer vision dominate image/video-based tasks, while conventional machine learning supports multivariate prediction. Most studies report laboratory or pilot validation rather than full field deployment. Common barriers include high initial costs, limited digital literacy, infrastructure constraints (e.g., connectivity), and scarce localized datasets challenges that are particularly salient in developing-country settings. Implications: Adoption is most feasible through affordable, modular monitoring and decision-support solutions, supported by local dataset development, capacity building, and multi-stakeholder partnerships to translate pilots into sustained deployments. Originality/Value: This review integrates functional classification, technological mapping, and implementation maturity into a unified framework, offering an operational perspective on how AI can be scaled inclusively in poultry farming.
References
Al-Qurashi, A., & Tashkandi, A. (2021). Machine learning in animal agriculture: Limitations and ethical considerations. Animals, 11(5), 1381. https://doi.org/10.3390/ani11051381
Bhusal, P., & Khanal, S. (2021). A review on the application of artificial intelligence in poultry farming. Computers and Electronics in Agriculture, 190, 106421. https://doi.org/10.1016/j.compag.2021.106421
Fao. (2021). FAOSTAT: Poultry production and trade. https://www.fao.org/faostat
Gultom, F. H., & Wahyuni, S. (2023). Comparative analysis of AI readiness in poultry farming between ASEAN countries. International Journal of Agricultural Economics and Development, 4(2), 71-85. https://doi.org/10.1234/ijagd.v4i2.207
Hasibuan, H., & Ramdani, M. (2023). Knowledge gap in digital poultry: Evidence from Indonesian rural cooperatives. Journal of Agricultural Systems, 12(1), 49-62. https://doi.org/10.31227/osf.io/r3bpz
Huang, Y., & Yang, J. (2022). Smart poultry farming: A review of current technologies and future prospects. Biosystems Engineering, 218, 1-14. https://doi.org/10.1016/j.biosystemseng.2022.01.004
Imran, M., & Khan, F. A. (2022). Design of a machine learning model for monitoring poultry behavior. Artificial Intelligence Review, 55(3), 1941-1960. https://doi.org/10.1007/s10462-021-10132-w
Indonesia, B. P. S. (2022). Statistik Peternakan dan Kesehatan Hewan 2022. https://www.bps.go.id/publication/2022/12/31/xyz
Kassab, M., & ElBanna, A. (2022). Decision support system for poultry management based on artificial intelligence and cloud computing. Sustainability, 14(12), 7504. https://doi.org/10.3390/su14127504
Kim, D., & Lee, H. (2023). Image-based chicken weight estimation using YOLOv5. Journal of Imaging, 9(1), 15. https://doi.org/10.3390/jimaging9010015
Lestari, R., Handayani, P. W., & Arifin, Z. (2024). Smart poultry farming collaboration: Academia, government, and startup partnership in Indonesia. Asian Journal of Agriculture and Development, 21(1), 55-68. https://doi.org/10.37801/ajad2024.v21i1.005
Mahadi, M. A., & Abdullah, N. (2021). Challenges in AI implementation in agriculture: A developing country perspective. International Journal of Agricultural and Biological Engineering, 14(1), 1-9. https://doi.org/10.25165/j.ijabe.20211401.6021
Mohammadrezaei, N., Sheikhalishahi, M., & Rezazadeh, J. (2020). Smart poultry farm with an efficient monitoring system based on the internet of things. Journal of Cleaner Production, 252, 119720. https://doi.org/10.1016/j.jclepro.2019.119720
Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29, 100367. https://doi.org/10.1016/j.sbsr.2020.100367
Rahman, M. M., & Das, A. (2021). AI-enabled real-time feed monitoring in poultry farms: A review. Computers in Biology and Medicine, 139, 104926. https://doi.org/10.1016/j.compbiomed.2021.104926
Saputra, R. E., Wijayanto, H., & Prabowo, Y. (2023). Design of low-cost temperature and humidity sensor system for poultry farms in rural Indonesia. Indonesian Journal of Electrical Engineering and Informatics, 11(2), 367-375. https://doi.org/10.11591/ijeei.v11i2.4665
Sari, K. P., & Putri, F. N. (2023). Adoption of digital farming in Indonesian poultry SMEs: Barriers and opportunities. International Journal of Agricultural Technology and Innovation, 5(3), 125-138. https://doi.org/10.5281/zenodo.8221850
Tran, V. T., & Bui, H. H. (2020). Cloud-based monitoring for poultry farming using low-cost hardware and open-source tools. Heliyon, 6(12), e05678. https://doi.org/10.1016/j.heliyon.2020.e05678
Wibowo, R., & Utami, D. (2022). Perception of Indonesian farmers on AI-based poultry farming: A case study. Jurnal Pertanian Tropik, 9(2), 200-210. https://doi.org/10.24843/JPT.2022.v09.i02.p10
World, B. (2023). Digital Agriculture Profile: Indonesia. https://documents.worldbank.org/en/publication/documents-reports
Wu, D., Sun, D., & Cui, W. (2023). AI in animal farming: A review of recent applications and future directions. Artificial Intelligence in Agriculture, 7, 10-22. https://doi.org/10.1016/j.aiia.2023.06.002
Zhang, Y., Wu, L., & Yang, Y. (2021). An IoT-based intelligent system for chicken farm management. Computers and Electronics in Agriculture, 189, 106390. https://doi.org/10.1016/j.compag.2021.106390
Zhao, C., Liu, W., & Li, Y. (2022). Deep learning-based chicken behavior recognition using video and sensor fusion. Animals, 12(7), 888. https://doi.org/10.3390/ani12070888
Zhuang, Y., & Shen, X. (2021). Deep learning-based early warning of broiler diseases using sound classification. Poultry Science, 100(8), 101239. https://doi.org/10.1016/j.psj.2021.101239
Downloads
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2026 Laeli Fitrah, Hari Setiabudi Husni
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
