ArticleData in brief2024
Image dataset for cattle biometric detection and analysis.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- A comprehensive image dataset of jute diseases.Data in brief · 2026Article
- From tradition to precision: leveraging digital tools to improve cattle health and welfare.Frontiers in veterinary science · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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