Monitoring horse behaviour with deep learning models

Claudia Giannone

doi:10.1080/01652176.2026.2665442

Monitoring horse behaviour with deep learning models

2026-04-28

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Emanuela Dalla Costa *

DOI：10.1080/01652176.2026.2665442

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Detailed analysis of stabled horse behaviour can reveal accurate information about its well-being. Advances in deep learning now allow these behaviours to be tracked without being invasive through the use of video data. This study evaluated a convolutional neural network for recognising standing, lying, and drinking behaviours in a horse housed in a wooden stall and recorded continuously over 29 consecutive days. Model predictions were compared with manually annotated ground truth data. Standing was detected with high precision (97.5%) and high recall (89.2%). Lying behaviour was classified with high precision (92.8%) but lower recall (63.1%). Activity patterns showed that standing dominated daily time budgets (>85%), lying accounted for 5-10%, and drinking occurred most often between 04:00 pm and 10:00 pm. These results demonstrate that deep learning can classify common equine behaviours from video, supporting its use in automated welfare monitoring. Future evaluations will explore the recognition of less frequent behaviours.

Keywords:

Equine welfare

computer vision

time budget

pose estimation

object detection

deep learning

equine behaviour

activity recognition

Monitoring horse behaviour with deep learning models

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