A dataset of deep learning training for multimodal head images of individual cows
Linxuan DU, Shanshan CAO, Tingting Liu, Wei SUN, Fantao Kong · China Scientific Data · 2025
With the advancement of agricultural modernization and precision agriculture, the intelligent and precision management of livestock farming has shown great potential for applications in areas such as animal health monitoring, farming process management, and precision farming. Leveraging computer vision and infrared imaging, image analysis-based non-contact health monitoring technologies enable real-time monitoring of cows’ physiological states and behavioral characteristics, which can support early disease detection and production management optimization. This study develops a dataset of multimodal head images of individual cows, comprising images collected from 50 cows under natural activity conditions and varying lighting environments (daytime and nighttime). The dataset includes 2,636 pairs of co-registered visible light (VIS) images and infrared thermographic (IRT) images, with precise annotations of cow head regions in the IRT images. Techniques such as blur detection, image quality assessment, and data augmentation were employed to optimize data quality, ensuring diversity and authenticity. This dataset can serve as a foundation for research on cross-modal learning, image pairing, and behavioral analysis, contributing to the development of intelligent monitoring systems and precision agriculture.