Innovative Livestock: A Survey of Artificial Intelligence Techniques in Livestock Farming Management

Authors

  • Maad M. Mijwil Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq
  • Oluwaseun Adelaja Information Communication and Technology Department, Lagos State University, Lagos, Nigeria
  • Amr Badr Faculty of Science, School of Science and Technology, University of New England, Armidale, NSW 2350, Australia
  • Guma Ali Department of Computer Science and Electrical Engineering, Muni University, Arua, Uganda
  • Bosco Apparatus Buruga Department of Library and Information Services, Muni University, Arua, Uganda
  • Pramila Pudasaini Associate Professor and Founder, Life Skills Education Institute Pvt. Ltd., Kathmandu, Nepal

DOI:

https://doi.org/10.31185/wjcms.206

Keywords:

Artificial intelligence, Machine Learning, Livestock, Agriculturalists, Farm management

Abstract

Modern technology has recently become a meaningful part of all life sectors, as software, sensors, smart machines, and expert systems are successfully integrated into the physical environment. This technology relies in its work on artificial intelligence techniques to make the right decisions at the right time. These technologies have a significant role in improving productivity, product quality, and industry outputs by significantly reducing human labour and errors that humans may cause. Artificial intelligence techniques are increasingly being integrated into animal husbandry and animal revolution management because they provide advantages and means that serve agriculturalists. These techniques monitor the emotional state of animals, milk production and herd management, feeding habits, the movement of animals, and their health status. AI-powered sensors can monitor the health of livestock and detect early signs of illness or stress to which they are exposed. Also, these techniques contribute to assisting agriculturalists in customising feeding programs, reducing waste, and improving product quality. This article will discuss the role of artificial intelligence techniques in animal control, farm management, disease surveillance, and sustainable resource optimisation practices.

References

Y. Duan, J. S. Edwards, and Y. K Dwivedi, “Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda,” International Journal of Information Management, vol.48, pp.63-71, October 2019. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

H. Lu, Y. Li, M. Chen, H. Kim, and S. Serikawa, “Brain Intelligence: Go beyond Artificial Intelligence,” Mobile Networks and Applications, vol.23, pp.368–375, September 2017. https://doi.org/10.1007/s11036-017-0932-8

N. N. M. Azam, M. A. Ismail, M. S. Mohamad, A. O. Ibrahim, and S. Jeba, “Classification of COVID-19 Symptoms Using Multilayer Perceptron,” Iraqi Journal For Computer Science and Mathematics, vol. 4, no. 4, pp.100–110, October 2023. https://doi.org/10.52866/ijcsm.2023.04.04.009

R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and F. Felice, “Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions,” Sustainability, vol.12, no.2, pp.1-26, January 2020. https://doi.org/10.3390/su12020492

S. Raschka, J. Patterson, and C. Nolet, “Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence,” Information, vol.11, no.4, pp.1-44, April 2020. https://doi.org/10.3390/info11040193

H. Omotunde and M. R. Mouhamed, “The Modern Impact of Artificial Intelligence Systems in Healthcare: A Concise Analysis,” Mesopotamian Journal of Artificial Intelligence in Healthcare, vol.2023, pp.66–70, November 2023. https://doi.org/10.58496/MJAIH/2023/013

B. Chhetri, L. M. Goyal, and M. Mittal, “How machine learning is used to study addiction in digital healthcare: A systematic review,” International Journal of Information Management Data Insights, vol.3, no.2, pp.100175, November 2023. https://doi.org/10.1016/j.jjimei.2023.100175

H. I. W. Al-Shahwani and A. K. Faieq, “The Benefit of Artificial Intelligence in the Analysis of Malignant Brain Diseases: A Mini Review,” Mesopotamian Journal of Artificial Intelligence in Healthcare, vol.2023, pp.57–60, November 2023. https://doi.org/10.58496/MJAIH/2023/011

F. F. Nah, R. Zheng, J. Cai, K. Siau, and L. Chen, “Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration,” Journal of Information Technology Case and Application Research, vol.25, no.3, July 2023. https://doi.org/10.1080/15228053.2023.2233814

A. Zador, S. Escola, B. Richards, B. Ölveczky, Y. Bengio, et al., “Catalyzing next-generation Artificial Intelligence through NeuroAI,” Nature Communications, vol.14, no.1597, pp.1-7, March 2023. https://doi.org/10.1038/s41467-023-37180-x

I. Bala, M. M. Mijwil, G. Ali, and E. Sadıkoğlu, “Analysing the Connection Between AI and Industry 4.0 from a Cybersecurity Perspective: Defending the Smart Revolution,” Mesopotamian Journal of Big data, vol.2023, pp.61-67, June 2023. https://doi.org/10.58496/MJBD/2023/009

S. Bi, C. Wang, J. Zhang, W. Huang, B. Wu, Y. Gong, and W. Ni, “A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries,” Sensors, vol.22, no.8, pp.1-22, April 2022. https://doi.org/10.3390/s22082991

V. Taecharungroj, ““What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter,” Big Data and Cognitive Computing, vol.7, no.1, pp.1-10, February 2023. https://doi.org/10.3390/bdcc7010035

M. M. Mijwil, K. K. Hiran, R. Doshi, M. Dadhich, AH. Al-Mistarehi, and I. Bala, “ChatGPT and the Future of Academic Integrity in the Artificial Intelligence Era: A New Frontier,” Al-Salam Journal for Engineering and Technology, vol. 2, no. 2, pp116-127, April 2023. https://doi.org/10.55145/ajest.2023.02.02.015.

S. S. Gill, M. Xu, P. Patros, H. Wu, R. Kaur, et al., “Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots,” Internet of Things and Cyber-Physical Systems, vol.4, pp.19-23, 2024. https://doi.org/10.1016/j.iotcps.2023.06.002

D. Zuidema, K. Kerns, and P. Sutovsky, “An Exploration of Current and Perspective Semen Analysis and Sperm Selection for Livestock Artificial Insemination,” Animals, vol.11, no.12, pp.1-15, December 2021. https://doi.org/10.3390/ani11123563

M. Ahmad, S. Abbas, A. Fatima, T. M. Ghazal, M. Alharbi, et al., “AI-Driven livestock identification and insurance management system,” Egyptian Informatics Journal, vol.24, no.3, pp.100390, September 2023. https://doi.org/10.1016/j.eij.2023.100390

S. Neethirajan and B. Kemp, “Digital Livestock Farming,” Sensing and Bio-Sensing Research, vol.32, pp.100408, June 2021. https://doi.org/10.1016/j.sbsr.2021.100408

O. Dogan, S. Tiwari, M. A. Jabbar, and S. Guggari, “A systematic review on AI/ML approaches against COVID-19 outbreak,” Complex & Intelligent Systems, vol.7, pp.2655–2678, July 2021. https://doi.org/10.1007/s40747-021-00424-8

M. J. Willemink, A. Varga-Szemes, U. J. Schoepf, M. Codari, et al., “Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence,” European Radiology Experimental, vol.5, no.12, pp.1-14, March 2021. https://doi.org/10.1186/s41747-021-00207-3

K. Linka, S. R. St. Pierre, and E. Kuhl, “Automated model discovery for human brain using Constitutive Artificial Neural Networks,” Acta Biomaterialia, vol.160, pp.134-151, April 2023. https://doi.org/10.1016/j.actbio.2023.01.055

N. Kanwisher, M. Khosla, and K. Dobs, “Using artificial neural networks to ask ‘why’ questions of minds and brains,” Trends in Neurosciences, vol.46, no.3, pp.240-254, January 2023. https://doi.org/10.1016/j.tins.2022.12.008

E. Rudenko, “Artificial Intelligence vs. Machine Learning vs. Deep Learning: Explaining The Difference,” Nix, May 2021. https://nix-united.com/blog/artificial-intelligence-vs-machine-learning-vs-deep-learning-explaining-the-difference/

D. Li and L. Du, “Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish,” Artificial Intelligence Review, vol.55, pp.4077–4116, November 2021. https://doi.org/10.1007/s10462-021-10102-3

S. K. Baduge, S. Thilakarathna, J. S. Perera, M. Arashpour, P. Sharafi, et al., “Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications,” Automation in Construction, vol.141, pp.104440, September 2022. https://doi.org/10.1016/j.autcon.2022.104440

J. Bao and Q. Xie, “Artificial intelligence in animal farming: A systematic literature review,” Journal of Cleaner Production, vol.331, pp.129956, January 2022. https://doi.org/10.1016/j.jclepro.2021.129956

P. Ezanno, S. Picault, G. Beaunée, X. Bailly, F. Muñoz, et al., “Research perspectives on animal health in the era of artificial intelligence,” Veterinary Research, vol.52, no.40, pp.1-15, March 2021. https://doi.org/10.1186/s13567-021-00902-4

R. Zhang, J. Ji, K. Zhao, J. Wang, M. Zhang, and M. Wang, “A Cascaded Individual Cow Identification Method Based on DeepOtsu and EfficientNet,” Agriculture, vol.13, no.2, pp.279, January 2023. https://doi.org/10.3390/agriculture13020279

S. Neethirajan, “Affective State Recognition in Livestock—Artificial Intelligence Approaches,” Animals, vol.12, no.6, pp.1-23, July 2022. https://doi.org/10.3390/ani12060759

K. McLennan and M. Mahmoud, “Development of an Automated Pain Facial Expression Detection System for Sheep (Ovis Aries),” Animals, von.9, no.4, pp.1-7, April 2019. https://doi.org/10.3390/ani9040196

Downloads

Published

2023-12-30

Issue

Vol. 2 No. 4 (2023)

Section

Computer

License

Copyright (c) 2023 Maad M. Mijwil, Oluwaseun Adelaja, Amr Badr, Guma Ali, Bosco Apparatus Buruga, Pramila Pudasaini

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
M. M. Mijwil, O. Adelaja, A. Badr, G. Ali, B. Apparatus Buruga, and P. Pudasaini, “Innovative Livestock: A Survey of Artificial Intelligence Techniques in Livestock Farming Management”, WJCMS, vol. 2, no. 4, pp. 99–106, Dec. 2023, doi: 10.31185/wjcms.206.