Integrated Model for Intelligent Monitoring and Diagnostics of Animal Health Based on IoT Technology for the Digital Farm

Serhii Semenov (), Dmytro Karlov (), Mikołaj Solecki, Igor Ruban, Andriy Kovalenko and Oleksii Piskarov
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Serhii Semenov: Institute of Security and Informatics, University of the National Education Commission, 30-084 Krakow, Poland
Dmytro Karlov: Department of Electronic Computers, Kharkiv National University of Radio Electronics, Nauky Ave. 14, 61166 Kharkiv, Ukraine
Mikołaj Solecki: Institute of Security and Informatics, University of the National Education Commission, 30-084 Krakow, Poland
Igor Ruban: Department of Electronic Computers, Kharkiv National University of Radio Electronics, Nauky Ave. 14, 61166 Kharkiv, Ukraine
Andriy Kovalenko: Department of Electronic Computers, Kharkiv National University of Radio Electronics, Nauky Ave. 14, 61166 Kharkiv, Ukraine
Oleksii Piskarov: Department of Electronic Computers, Kharkiv National University of Radio Electronics, Nauky Ave. 14, 61166 Kharkiv, Ukraine

Sustainability, 2025, vol. 17, issue 18, 1-25

Abstract: The object of the research is the process of intelligent monitoring and diagnosis of animal health using IoT technology in the context of a digital farm. The problem lies in the absence of an integrated approach that can provide near-real-time assessment of an animal’s physiological and behavioral state, predict potential health risks, and adapt decision-making algorithms to specific species and environmental conditions. Traditional monitoring methods rely heavily on periodic manual inspection and limited sensor data, which reduces the timeliness and accuracy of diagnostics, especially for large-scale farms. To address this issue, a comprehensive model is proposed that integrates an IoT-based tag device for livestock, a data collection and transmission system, and an intelligent analysis module. The system utilizes statistical profiling to create baseline health parameters for each animal, applies anomaly detection methods to identify deviations, and leverages machine learning algorithms to predict health deterioration. The novelty of the approach lies in the combination of individualized baseline modeling, continuous sensor-based monitoring, and adaptive decision-making for early intervention. The approach scales across farm sizes and multi-sensor setups, making it practical for precision livestock farming. From a sustainability perspective, the approach enables earlier and more targeted interventions that can reduce unnecessary treatments, avoid preventable productivity losses, and support animal welfare. The design uses energy-aware IoT practices (on-device 60 s aggregation with one-minute uplinks) and lightweight analytics to limit device power use and network load, aligning the system with resource-efficient livestock operations.

Keywords: sustainability; sustainable livestock; animal welfare; resource efficiency; energy-aware IoT; precision livestock farming; anomaly detection; explainable machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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