Integration of Machine Learning, IoT, and Geofencing for Livestock Management: A Review

• Faisal N. YERIMA

  Author

• Mohammed S. ISMAIL

  Author

• Murtala ISMAIL

  Author

• Abdulmalik R. MUSA

  Author

An essential part of agriculture is livestock management, which calls for effective animal tracking and monitoring to guarantee the production, health, and well-being of the animals. Livestock management is one of the areas in agriculture that has made a substantial contribution in the global GDP. More to that, a lot of households around the world generate a lot of income from sales of meat, dairy, milk, butter, cheese, wool, hides and other by-products. In numerous regions, the practice of livestock farming is not merely an economic venture; it has deep cultural and traditional significance. It is at the heart of the identity and livelihood activities for many, how indigenous peoples, pastoralists and farming communities are resilient. Also, in much of the world, especially in pastoral systems that support diverse cultural landscapes and ecosystems, livestock play a role in biodiversity and maintaining land use. The field of livestock management has changed as a result of recent developments and technological innovations in Artificial Intelligence (AI), more especially machine learning (ML), the Internet of Things (IoT), and geofencing technologies. These technological innovations improve productivity, reduce disease transmission, and enable more sustainable and efficient livestock farming. Several studies have proposed various schemes and techniques for livestock management. This study presents a comprehensive review of the state-of-the-art AI schemes used in solving most of the issues related to livestock management and examines the advantages, difficulties, and potential future directions of combining ML, IoT, and geofencing for cattle management.

• 66_Yerima et al. (2026)

Copyright (c) 2026 Faisal N. YERIMA, Mohammed S. ISMAIL, Murtala ISMAIL, Abdulmalik R. MUSA (Author)

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