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Related Experiment Video

Updated: Jul 16, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
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Published on: October 27, 2023

Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using

Nikolay Kiktev1, Danylo Hradoboiev1, Mykola Pravilov1

  • 1Department of Automation and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, 15 Heroiv Oborony Str., 03041 Kyiv, Ukraine.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
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Sensors (Basel, Switzerland)·2022

Intelligent sensor technologies and machine learning enhance farm animal care, from feed production quality control to behavior monitoring and veterinary diagnostics. This data-driven approach boosts animal welfare and agricultural sustainability.

Area of Science:

  • Agricultural Technology
  • Animal Science
  • Veterinary Medicine

Background:

  • Modern livestock management requires advanced methods for monitoring animal health, nutrition, and behavior.
  • Traditional methods often lack the precision and efficiency needed for large-scale operations.
  • The integration of intelligent sensor systems offers a transformative solution.

Purpose of the Study:

  • To review and structure modern intelligent sensor technologies in animal husbandry, feed production, and veterinary medicine.
  • To highlight the role of machine learning, computer vision, and sensor systems in improving farm animal assessment.
  • To explore applications in feed quality, animal behavior analysis, and veterinary diagnostics.

Main Methods:

  • Analysis of machine learning models, including neural networks and computer vision (e.g., YOLO).
Keywords:
CNNDLMLaccelerometryanimalsbiosensoricsdatastreamdiagnosticspremixspectrometrytelemetrytensometryvideomonitoring

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  • Review of sensor systems such as visual sensors, ultra-wideband (UWB), accelerometers, and microwave sensors.
  • Examination of data analysis techniques for multimodal data, medical imaging, and radiometric diagnosis.

    • Main Results:

    • Intelligent sensors optimize feed premix quality control and production processes.
    • Computer vision and UWB systems accurately track animal location and classify behaviors, detecting anomalies.
    • Machine learning aids in automated veterinary diagnostics from medical images and multimodal data, enabling proactive disease detection.

    Conclusions:

    • The integration of intelligent systems facilitates a shift towards data-driven livestock management.
    • These technologies significantly enhance animal welfare and improve the efficiency and sustainability of agricultural production.
    • Intelligent sensors are pivotal in advancing precision agriculture and animal health monitoring.