Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Biosensors in precision livestock farming in dairy production: decoding animals' needs

  • Mingyung Lee (Department of Animal Science, Texas A&M University) ;
  • Luis O. Tedeschi (Department of Animal Science, Texas A&M University) ;
  • Seongwon Seo (Department of Animal Biosystem Sciences, Chungnam National University)
  • Received : 2026.02.16
  • Accepted : 2026.03.16
  • Published : 2026.04.01
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Abstract

Precision livestock farming in dairy production is advancing through biosensor-based monitoring that converts frequent, longitudinal measurements into actionable information to support animal-level decision-making under commercial conditions. This review summarizes biosensors for precision dairy farming with a systems perspective that connects sensing, data transfer, analytics, and visualization. Biosensors can be categorized by sensing locus as at-animal, near-animal, and from-animal to clarify practical tradeoffs among invasiveness, scalability, maintenance burden, and diagnostic specificity. The review also describes how information should progress from raw signals to interpretable indicators and decision-support outputs, emphasizing that farm value depends on reliable interpretation and timely intervention rather than on measurement alone. Key applications are synthesized across nutrition and feeding behavior, reproduction (including estrus and calving), health monitoring (such as mastitis, lameness, and metabolic disorders), welfare assessment, and environmental sustainability, highlighting where different modalities best support screening, early warning, and confirmatory detection. Finally, the review discusses on-farm barriers, including missing data, sensor drift, attachment stability, communication failures, and alert fatigue, and proposes future directions in standardization, interoperability, and artificial intelligence-enabled decision support to strengthen end-to-end system reliability, scalability, and economic sustainability under commercial conditions.

Keywords

Acknowledgement

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) and Korea Smart Farm R&D Foundation (KosFarm) through Smart Farm Innovation Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration (RDA; RS-2025-02218887).

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