Smart Cattle Monitoring Using Computer Vision to Detect Physiological, Behavioral, and Physical Characteristics

This study investigates the use of computer vision as a non-invasive, scalable approach for monitoring key physiological, behavioral, and physical indicators of beef cattle health and welfare. The main goal was to develop automated, camera-based frameworks capable of replacing or augmenting manual or contact-based monitoring methods commonly used in livestock management.

Three integrated systems were developed and validated under real farm conditions: (1) breathing-rate detection, (2) drinking-behavior estimation, and (3) body condition scoring (BCS). For respiration monitoring, a learning-based motion magnification and pose-estimation pipeline was implemented to amplify low-flank movements associated with breathing. Adaptive signal analysis and a hybrid winner strategy were applied to extract breathing frequencies from motion signals. The model achieved a mean bias of 1.84% and limits of agreement within ±20% when compared with manually counted respiration, with accuracy remaining stable across lighting conditions and animal variations.

The drinking-behavior framework utilized pose estimation to track head and neck keypoints and employed Long Short-Term Memory (LSTM) and Transformer networks to classify drinking events from video sequences. The LSTM achieved 95.5% accuracy and an F1-score of 0.97. Behavioral features derived from the classified events, including duration and frequency, were integrated with water flow–sensor data to predict daily water intake (R² = 0.81). Results revealed increased water consumption during estrus periods, highlighting the biological relevance of behavioral metrics derived from vision data.

The automated BCS framework combined multi-view image analysis and feature extraction from anatomically significant regions such as the spine, tail head, and whole body, identified by a YOLO detector. Features such as tail-head angle, spine ridge visibility, fat-knob counts, and contour roundness were used in classification models, achieving up to 0.93 accuracy with Random Forests.

Collectively, these three studies demonstrate that camera-based monitoring can provide continuous, objective, and interpretable assessment of cattle physiology, behavior, and condition. The findings establish a foundation for integrated, AI-driven livestock management systems capable of supporting early disease detection, welfare assessment, and data-driven decision-making. This research advances precision livestock farming toward autonomous, welfare-focused, and sustainable beef production.