Evaluating the Use of Artificial Intelligence and Machine Learning in the
Livestock Industry: A Review
Yasir Nawaz, Advanced Robotics & Automation Lab, Department of Mechatronics, University of Engineering and Technology, Peshawar, Pakistan.
Mubasher Ullah, Department of Energy Management and Sustainability, University of Engineering and Technology, Peshawar, Pakistan.
Ismat Ullah, Department of Industrial Engineering, University of Engineering and Technology, Peshawar, Pakistan.
Zeeshan Khan, Department of Mechatronics, University of Engineering and Technology, Peshawar, Pakistan.
Muhammad Usman Qadir, Advanced Robotics & Automation Lab, Department of Mechatronics, University of Engineering and Technology, Peshawar, Pakistan.
Izhar Ul Haq, Advanced Robotics & Automation Lab, Department of Mechatronics, University of Engineering and Technology, Peshawar, Pakistan.
Corresponding Author:
Yasir Nawaz (ynawaz.mct@uetpeshawar.edu.pk)
Abstract:
AI and ML can improve livestock management (LSM). This review paper examines how AI and ML can improve LSM, including disease detection and prediction, nutrition optimization, behavior monitoring and welfare evaluation, environmental monitoring, reproductive management, and traceability management. It covers the benefits and drawbacks of using AI and ML in LSM. Before LSM could fully use AI and ML, the review outlines various constraints and problems. These include data availability and quality, specialist knowledge and experience, ethical and regulatory concerns, and preserving traditional animal husbandry knowledge. The review also proposes further research and stakeholder collaboration to maximize AI and ML benefits in LSM. These potentials include the integration of AI and ML approaches, the developing of user-friendly and accessible technologies, and stakeholder collaboration to ensure equitable benefit distribution and ethical considerations. The review emphasizes AI and ML's promise to improve LSM and the necessity for continuing research and collaboration to overcome the obstacles and limits. The study highlights the potential of AI and ML in improving LSM practices to overcome the hurdles and constraints associated with applying these technologies. It underscores the need for continuing research and collaboration.
Keywords:
Artificial Intelligence (AI); Machine learning (ML); LiveStock Management (LSM); Deep Learning (DL); Explainable AI (XAI); Natural Language Processing (NLP)