Internet of things and machine learning techniques in poultry health and welfare management: A systematic literature review

Ojo, Rasheed O; Ajayi, Anuoluwapo O; Owolabi, Hakeem A; Oyedele, Lukumon O; Akanbi, Lukman A

Abstract

The advent of digital technologies has brought substantial improvements in various domains. This article provides a comprehensive review of research emphasizing AI-enabled IoT applications in poultry health and welfare management. This study focused on poultry welfare since modern poultry management is confronted with issues relating to standardized parameters for welfare assessment and robust monitoring systems, particularly for broilers' health and disease outbreak prevention. Evidence has shown that modern digital technologies have high possibilities for intelligent automation of current and future poultry management operations to facilitate high-quality and low-cost poultry production. Therefore, this study presents a systematic review of the current state-of-the-art AI-enabled IoT systems and their recent advances in developing intelligent systems in this domain. Also, the study provides an overview of the critical applications of identified digital technologies in poultry welfare management. Lastly, the study discusses the challenges and opportunities of AI and IoT in poultry farming.

Citation

Ojo, R. O., Ajayi, A. O., Owolabi, H. A., Oyedele, L. O., & Akanbi, L. A. (2022). Internet of things and machine learning techniques in poultry health and welfare management: A systematic literature review. Computers and Electronics in Agriculture, 200, Article 107266. https://doi.org/10.1016/j.compag.2022.107266

• Journal Article Type: Review
• Acceptance Date: Jul 24, 2022
• Online Publication Date: Jul 30, 2022
• Publication Date: Sep 1, 2022
• Deposit Date: Aug 8, 2022
• Publicly Available Date: Jul 31, 2024
• Journal: Computers and Electronics in Agriculture
• Print ISSN: 0168-1699
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 200
• Article Number: 107266
• DOI: https://doi.org/10.1016/j.compag.2022.107266
• Keywords: Behavioral parameters; environmental parameters; deep learning; computer vision
• Public URL: https://uwe-repository.worktribe.com/output/9851288
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0168169922005798?via%3Dihub