[
Abdanan Mehdizadeh S., Sari M., Orak H., Pereira D.F., Nääs I.D. (2023). Classifying chewing and rumination in dairy cows using sound signals and machine learning. Animals, 13: 2874.
]Search in Google Scholar
[
Aharwal B., Roy B., Meshram S., Yadav A. (2023). Worth of artificial intelligence in the epoch of modern livestock farming: A review. Agricult. Sci. Digest-A Res. J., 43: 1–9.
]Search in Google Scholar
[
Alanezi M.A., Shahriar M.S., Hasan M.B., Ahmed S., Yusuf A., Bouchekara H.R. (2022) Livestock management with unmanned aerial vehicles: A review. IEEE Access., 10: 45001–45028.
]Search in Google Scholar
[
Alizadeh H., Hasani-Bafarani A., Parvin H., Minaei B., Kangavari M.R. (2008). Dairy cattle judging: An innovative application for fuzzy expert system. In: Proceedings of the World Congress on Engineering and Computer Science 2008, pp. 22–24.
]Search in Google Scholar
[
Amini S., Taki M., Rohani A. (2020). Applied improved RBF neural network model for predicting the broiler output energies. Appl. Soft Comput., 87: 106006.
]Search in Google Scholar
[
Ardekani A.S., Heydari Z., Yazdi A.F. (2017). The effects of the targeted subsidies scheme on financial performance and information content of earnings: Evidence from Tehran Stock Exchange. J. Econom. Manag. Perspect., 11: 1028–1037.
]Search in Google Scholar
[
Avizheh M., Dadpasand M., Dehnavi E., Keshavarzi H. (2023). Application of machine-learning algorithms to predict calving difficulty in Holstein dairy cattle. Anim. Prod. Sci., 63: 1095–1104.
]Search in Google Scholar
[
Azizi M., Naeemipour H., Farhangfar S.H., Bashtani M. (2024). Comparison of artificial neural network performance with some mathematical functions in predicting of lactation curve of Iranian dairy cows. Anim. Sci. J., 36: 133–142.
]Search in Google Scholar
[
Azizi Z., Shahrbabak H.M., Rafat S.A., Moradi M. (2020). Study of population structure and genetic prediction of buffalo from different provinces of Iran using machine learning method. J. Buffalo Sci., 9: 48–59.
]Search in Google Scholar
[
Bagheri H., Tapak L., Karami M., Hosseinkhani Z., Najari H., Karimi S., Cheraghi Z. (2020). Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019. PloS One, 15(5): e0232910.
]Search in Google Scholar
[
Bagherpour H., Mohamadi H. (2014). Challenges and prospects of precision agriculture in Iran. Int. J. Sci. Emerg. Technol., 17: 1–8.
]Search in Google Scholar
[
Cantú-Ortiz F.J., Galeano Sánchez N., Garrido L., Terashima-Marin H., Brena R.F. (2020). An artificial intelligence educational strategy for the digital transformation. Int. J. Interact. Design Manufact. (IJIDeM), 14: 1195–1209.
]Search in Google Scholar
[
Charatsari C., Lioutas E.D., De Rosa M., Vecchio Y. (2022). Technological innovation and agrifood systems resilience: The potential and perils of three different strategies. Front. Sust. Food Syst., 6: 872706.
]Search in Google Scholar
[
Chaudhry A.A., Mumtaz R., Zaidi S.M., Tahir M.A., School S.H. (2020). Internet of things (IoT) and machine learning (ML) enabled livestock monitoring. Proc. IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), 14.12.2020, pp. 151–155.
]Search in Google Scholar
[
Das K.N., Bansal J.C., Deep K., Nagar A.K., Pathipooranam P., Naidu R.C. (2019). Editors. Soft Computing for Problem Solving: SocProS 2018, Vol. 2. Springer Nature.
]Search in Google Scholar
[
Dayoub M., Shnaigat S., Tarawneh R.A., Al-Yacoub A.N., Al-Barakeh F., Al-Najjar K. (2024). Enhancing animal production through smart agriculture: possibilities, hurdles, resolutions, and advantages. Ruminants, 4: 22–46.
]Search in Google Scholar
[
De Vries A., Bliznyuk N., Pinedo P. (2023). Invited review: Examples and opportunities for artificial intelligence (AI) in dairy farms. Appl. Anim. Sci., 39: 14–22.
]Search in Google Scholar
[
Ellis J.L., Jacobs M., Dijkstra J., Van Laar H., Cant J.P., Tulpan D., Ferguson N. (2020). Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data. Animal, 14(S2): 223–237.
]Search in Google Scholar
[
Ezanno P., Picault S., Beaunée G., Bailly X., Muñoz F., Duboz R., Monod H., Guégan J.F. (2021). Research perspectives on animal health in the era of artificial intelligence. Vet. Res., 52: 1–5.
]Search in Google Scholar
[
FAO (Food and Agriculture Organization of the United Nations) (2021). Food and Agriculture Organization Corporate Statistical Database (FAOSTAT); FAO: Rome, Italy, 2021. Available online: http://www.fao.org/faostat/en/#home (accessed on 12 January 2022).
]Search in Google Scholar
[
Fuentes S., Viejo C.G., Tongson E., Dunshea F.R. (2022). The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence. Anim. Health Res. Rev., 23: 59–71.
]Search in Google Scholar
[
Georgopoulos V.P., Gkikas D.C., Theodorou J.A. (2023). Factors influencing the adoption of artificial intelligence technologies in agriculture, livestock farming and aquaculture: A systematic literature review using PRISMA 2020. Sustainability, 15: 16385.
]Search in Google Scholar
[
Ghaderi Z.M., Bahreini B.M., Fayaz M., Sharifi S. (2016). Association between calving interval and productive traits in dairy cattle over different inseminations using artificial neural network. Rumin. Res., 3: 169–188.
]Search in Google Scholar
[
Gholami M., Javidan R. (2017). An intelligent model for prediction of brucellosis. Proc: IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2017, pp. 0570–0576.
]Search in Google Scholar
[
Ghotoorlar S.M., Ghamsari S.M., Nowrouzian I., Ghotoorlar S.M., Ghidary S.S. (2012). Lameness scoring system for dairy cows using force plates and artificial intelligence. Vet. Rec., 170: 126.
]Search in Google Scholar
[
Hamidi S.P., Mohammadabadi M.R., Foozi M.A., Nezamabadi-Pour H.(2017). Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. J. Livest. Sci. Technol., 5: 53–61.
]Search in Google Scholar
[
Heydari R. (2023). A flexible combination forecast method for modeling agricultural commodity prices: a case study Iran’s livestock and poultry meat market. J Agric. Econom. Develop., 37: 177–202.
]Search in Google Scholar
[
Hosseinzadeh-Bandbafha H., Nabavi-Pelesaraei A., Shamshirband S. (2017). Investigations of energy consumption and greenhouse gas emissions of fattening farms using artificial intelligence methods. Environ. Progress Sustain. Energ., 36: 1546–1559.
]Search in Google Scholar
[
ICCIMA (Iran Chamber of Commerce, Industries, Mines and Agriculture) (2023). Iran Agriculture Sector Guide. https://cic.tccim.ir/persian/files/Agriculture-EN.pdf.
]Search in Google Scholar
[
Ignatova E. (2020). Professional standard as an incentive for the development of polytechnic education. Proc: International Scientific Conference “Far East Con” (ISCFEC 2020), 17.03.2020, Atlantis Press, pp. 1833–1837.
]Search in Google Scholar
[
Kalhor T., Rajabipour A., Akram A., Sharifi M. (2016). Modeling of energy ratio index in broiler production units using artificial neural networks. Sust. Energ. Technol. Assess., 17: 50–55.
]Search in Google Scholar
[
Keshavarzi H., Sadeghi-Sefidmazgi A., Mirzaei A., Ravanifard R. (2020). Machine learning algorithms, bull genetic information, and imbalanced datasets used in abortion incidence prediction models for Iranian Holstein dairy cattle. Prevent. Vet. Med., 175: 104869.
]Search in Google Scholar
[
Khojastehkey M., Aslaminejad A.A., Shariati M.M., Dianat R. (2015). Pelt pattern classification of new born lambs using image processing and artificial neural network. Global J. Anim. Sci. Res., 3: 321–328.
]Search in Google Scholar
[
Khojastehkey M., Abbasi M.A., Akbari Sharif A., Hasani A.M. (2016). Weight estimation of Zandi lambs using digital image processing. Anim. Sci. J., 29: 99–104.
]Search in Google Scholar
[
Khojastehkey M., Aslaminejad A.A., Jafari Arvari A.R. (2019). Curl size and pelt color determination of Zandi lambs using image processing and artificial neural network. Iranian J. Appl. Anim. Sci., 9: 105–113.
]Search in Google Scholar
[
Khojastehkey M., Sadeghipanah A., Asadzadeh N., Aghashahi A., Keikhah Saber M., Bitaraf Sani M., Esmaeilkhanian S. (2022). Prediction of body weight of Sistani cows using computer vision. Anim. Prod. Res., 11: 55–66.
]Search in Google Scholar
[
Kianmehr M.H., Ghasemi M., Ghadirnejad K., Vartaparvar V. (2013). Design, construction and evaluation of the intelligent feeding device for dairy cows. Proc. 7th National Congress of Agricultural Machinery and Mechanization. 14–16.09.2013, Shiraz University.
]Search in Google Scholar
[
Lovarelli D., Bacenetti J., Guarino M. (2020). A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production? J. Clean. Prod., 262: 121409.
]Search in Google Scholar
[
Masoudi H. (2016). Robotics; a new field for innovation and entrepreneurship development in the animal husbandry sector. J. Entrepreneur. Agricult., 3: 19–37.
]Search in Google Scholar
[
Masoudi H., Rouhani A. (2013). Designing an automatic fodder feeding system for livestock. Proc. 7th National Congress of Agricultural Machinery and Mechanization. 14–16.09.2013, Shiraz University.
]Search in Google Scholar
[
McCarthy J., Minsky M.L., Rochester N., Shannon C.E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27: 12.
]Search in Google Scholar
[
Melak A., Aseged T., Shitaw T. (2024). The influence of artificial intelligence technology on the management of livestock farms. Int. J. Distrib. Sensor Networks, https://doi.org/10.1155/2024/8929748.
]Search in Google Scholar
[
Meshgi B., Hanafi-Bojd A.A., Fathi S., Modabbernia G., Meshgi K., Shadman M. (2024). Multi-scale habitat modeling framework for predicting the potential distribution of sheep gastrointestinal nematodes across Iran’s three distinct climatic zones: a MaxEnt machine-learning algorithm. Sci. Rep., 14: 2828.
]Search in Google Scholar
[
Ministry of Agriculture Jihad (MAJ) (2020). Statistical Book of Agriculture of Iran. Iranian Statistical Centre, Tehran, Iran.
]Search in Google Scholar
[
Mohammadpour A., Samaei M.R., Baghapour M.A., Alipour H., Isazadeh S., Azhdarpoor A., Khaneghah A.M. (2024). Nitrate concentrations and health risks in cow milk from Iran: Insights from deterministic, probabilistic, and AI modeling. Environ. Pollut., 341: 122901.
]Search in Google Scholar
[
Montazeri-Najafabadi M., Ghaderi-Zefrehei M. (2021). Data mining of some factors affecting dystocia in Iranian dairy cows. Anim. Prod., 10: 13–24.
]Search in Google Scholar
[
Morrone S., Dimauro C., Gambella F., Cappai M.G. (2022). Industry 4.0 and precision livestock farming (PLF): an up to date overview across animal productions. Sensors, 22: 4319.
]Search in Google Scholar
[
Neethirajan S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing Bio-Sensing Res., 29: 100367.
]Search in Google Scholar
[
Neethirajan S. (2023 a). Artificial intelligence and sensor technologies in dairy livestock export: charting a digital transformation. Sensors, 23: 7045.
]Search in Google Scholar
[
Neethirajan S. (2023 b). Harnessing the metaverse for livestock welfare: unleashing sensor data and navigating ethical frontiers. Pre-prints, 2023040409.
]Search in Google Scholar
[
Neethirajan S. (2023 c). The significance and ethics of digital livestock farming. AgriEng., 5: 488–505.
]Search in Google Scholar
[
Neethirajan S., Kemp B. (2021 a). Digital livestock farming. Sensing Bio-Sensing Res., 32: 100408.
]Search in Google Scholar
[
Neethirajan S., Kemp B. (2021 b). Digital twins in livestock farming. Animals, 11: 1008.
]Search in Google Scholar
[
Norouzian M.A., Bayatani H., Alavijeh M.V. (2021). Comparison of artificial neural networks and multiple linear regression for prediction of dairy cow locomotion score. Vet. Res. Forum, 12: 33.
]Search in Google Scholar
[
Oliveira D.A., Pereira L.G., Bresolin T., Ferreira R.E., Dorea J.R. (2021). A review of deep learning algorithms for computer vision systems in livestock. Livest. Sci., 253: 104700.
]Search in Google Scholar
[
Oliveira R.C.D., Silva R.D.S.E. (2023). Artificial intelligence in agriculture: benefits, challenges, and trends. Appl. Sci., 13: 7405.
]Search in Google Scholar
[
Orandi B. (2020). The situation of the Internet of things in Iran. Available online: http://www.majazi.ir/general_content/90521/swd_id/13825/unitWdId/53777/ (Accessed: January 7, 2020).
]Search in Google Scholar
[
Rezvani S.M., Shamshiri R.R., Moghaddam J.J., Balasundram S.K., Hameed I.A. (2022). Digital agriculture in Iran: Use cases, opportunities, and challenges. Digital Agriculture, Methods and Applications. https://doi.org/10.5772/intechopen.103967.
]Search in Google Scholar
[
Sadeghi M., Banakar A., Minaei S., Orooji M., Shoushtari A., Li G. (2023). Early detection of avian diseases based on thermography and artificial intelligence. Animals, 13: 2348.
]Search in Google Scholar
[
Salas J., Patterson G., de Barros Vidal F. (2022). A systematic mapping of artificial intelligence solutions for sustainability challenges in Latin America and the Caribbean. IEEE Latin America Transactions, 20: 2312–2329.
]Search in Google Scholar
[
Sefeedpari P., Rafiee S., Akram A. (2013). Application of artificial neural network to model the energy output of dairy farms in Iran. Int. J. Energ. Technol. Policy, 9: 82–91.
]Search in Google Scholar
[
Shahinfar S., Mehrabani-Yeganeh H., Lucas C., Kalhor A., Kazemian M., Weigel K.A. (2012). Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Computat. Math. Method. Med., https://doi.org/10.1155/2012/127130.
]Search in Google Scholar
[
Shahriary G., Mir Y. (2016). Application of artificial neural network model in predicting price of milk in Iran. Modern Appl. Sci., 10: 173–178.
]Search in Google Scholar
[
Shirzadifar A., Miar Y., Plastow G., JA J.B., Li C., Fitzsimmons C., Riazi M., Manafiazar G. (2023). A machine learning approach to predict the most and the least feed-efficient groups in beef cattle. Smart Agricult. Technol., 5: 100317.
]Search in Google Scholar
[
Sotoudeh M., Alavi N., Zarrineh A. (2024). Improving online livestock health monitoring system using machine learning, AI, ANN, IoT and sound-based technologies: A pilot study. Preprints.
]Search in Google Scholar
[
Taheri F., D’Haese M., Fiems D., Azadi H. (2022). Facts and fears that limit digital transformation in farming: Exploring barriers to the outreach of wireless sensor networks in Southwest Iran. Plos One, 17(12):e0279009.
]Search in Google Scholar
[
Tahmoorespur M., Ahmadi H. (2012). A neural network model to describe weight gain of sheep from genes polymorphism, birth weight and birth type. Livest. Sci., 148: 221–226.
]Search in Google Scholar
[
Tedeschi L.O. (2022). ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science. J. Anim. Sci., 100: skac111.
]Search in Google Scholar
[
Tedeschi L.O., Greenwood P.L., Halachmi I. (2021). Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming. J. Anim. Sci., 99: skab038.
]Search in Google Scholar
[
Turing A.M. (1950). Computing machinery and intelligence. MIND, A Quarterly Review of Psychology and Philosophy, London, U.K., Oxford Univ. Press.
]Search in Google Scholar
[
Vaysi A., Pashaki S.G., Rohani A., Khojastehpour M. (2023). Variation and spatial distribution of emissions from livestock manure management in Iran: an evaluation and location analysis. https://doi.org/10.21203/rs.3.rs-3035513/v1.
]Search in Google Scholar
[
Zhang M., Wang X., Feng H., Huang Q., Xiao X., Zhang X. (2021). Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring. J. Clean. Prod., 312: 127712.
]Search in Google Scholar
