Citation

Ashraf, Arselan and Nisa, Syed Qamrun and Ashraf, Afreen and Gunawan, Teddy Surya and Sophian, Ali (2026) Precision Livestock Farming: The Role of Internet of Things (IOT) in Estrus Detection and Management. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 14 (1). ISSN 2089-3272

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Abstract

The livestock industry represents a vital sector of the global economy, where reproductive management plays a key role in sustaining productivity and profitability. Estrus detection, a critical component of reproductive efficiency, directly influences breeding success and overall herd performance. Recent advancements in the Internet of Things (IoT) have introduced new opportunities to enhance estrus detection through the integration of sensors, data analytics, and machine learning algorithms. This review explores the potential of IoT-based technologies in livestock estrus detection, focusing on a wide range of approaches including wearable and non-wearable sensors, data collection frameworks, and advanced analytical methods. Commercial IoT-based estrus detection systems are also examined, alongside comparative evaluations of detection performance, advantages, and limitations. Key challenges such as battery life, connectivity, network coverage, data security, privacy, and cost scalability are discussed in detail. Furthermore, the paper highlights future directions, including the integration of IoT with precision livestock farming and the role of emerging technologies in improving animal welfare and production efficiency. Overall, this review provides a comprehensive overview of IoT-based estrus detection, outlining current progress, practical implications, and recommendations for future research and implementation.

Item Type:	Article
Uncontrolled Keywords:	Estrus Detection, Electronics, Internet of Things (IoT), Livestock, Livestock Farming
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 May 2026 04:55
Last Modified:	04 May 2026 04:59
URII:	http://shdl.mmu.edu.my/id/eprint/15873

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