IoT technologies for livestock management: A review of present status, opportunities, and future trends

Akhigbe, Bernard Ijesunor; Munir, Kamran; Akinade, Olugbenga; Akanbi, Lukman; Oyedele, Lukumon O.

Abstract

The world population currently stands at about 7 billion amidst an expected increase in 2030 from 9.4 billion to around 10 billion in 2050. This burgeoning population has continued to influence the upward demand for animal food. Moreover, the management of finite resources such as land, the need to reduce livestock contribution to greenhouse gases, and the need to manage inherent complex, highly contextual, and repetitive day-to-day livestock management (LsM) routines are some examples of challenges to overcome in livestock production. The Internet of Things (IoT)’s usefulness in other vertical industries (OVI) shows that its role will be significant in LsM. This work uses the systematic review methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to guide a review of existing literature on IoT in OVI. The goal is to identify the IoT’s ecosystem, architecture, and its technicalities—present status, opportunities, and expected future trends—regarding its role in LsM. Among identified IoT roles in LsM, the authors found that data will be its main contributor. The traditional approach of reactive data processing will give way to the proactive approach of augmented analytics to provide insights about animal processes. This will undoubtedly free LsM from the drudgery of repetitive tasks with opportunities for improved productivity.

Citation

Akhigbe, B. I., Munir, K., Akinade, O., Akanbi, L., & Oyedele, L. O. (2021). IoT technologies for livestock management: A review of present status, opportunities, and future trends. Big Data and Cognitive Computing, 5(1), Article 10. https://doi.org/10.3390/bdcc5010010

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2021
• Online Publication Date: Feb 26, 2021
• Publication Date: Mar 1, 2021
• Deposit Date: Jun 25, 2021
• Publicly Available Date: Jul 1, 2021
• Journal: Big Data and Cognitive Computing
• Electronic ISSN: 2504-2289
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 1
• Article Number: 10
• DOI: https://doi.org/10.3390/bdcc5010010
• Public URL: https://uwe-repository.worktribe.com/output/7150785