Abstract
The dataset of cattle biometric features is a pivotal asset for improving livestock management and promoting smart agriculture innovation. We obtained a dataset of images capturing the side and back views of Horqin yellow cattle from a farm in eastern Inner Mongolia, China. These data consist of images of 72 free-range Horqin yellow cattle taken with a mobile camera on the grasslands. Each cattle is accompanied by detailed annotations, including oblique body length, withers height, heart girth, hip length, as well as body weight among other crucial data points. This information is considered as high-quality biological feature data. In the field of computer vision, utilizing this dataset can facilitate the construction of deep learning models to develop an automated livestock monitoring system. The aim is to enhance management efficiency and operational effectiveness within the livestock industry. By integrating biological feature information, specific model tools can be employed for body condition assessment and health monitoring research. This approach enables the effective identification and prevention of disease conditions, ultimately providing a deeper level of care and support for livestock welfare and health. The cattle dataset offers support for smart agriculture by enabling the development of intelligent farm management systems. These systems facilitate real-time alerts for livestock health and environmental monitoring. This advancement will drive the modernization and digitization of animal husbandry, fostering agricultural intelligence and sustainable development.