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Dataset of cattle biometrics through muzzle images
- Source :
- Data in Brief, Vol 53, Iss , Pp 110125- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- The Cattle Biometrics Dataset is the result of a rigorous process of data collecting, encompassing a wide range of cattle photographs obtained from publicly accessible cattle markets and farms. The dataset provided contains a comprehensive collection of more than 8,000 annotated samples derived from several cow breeds. This dataset represents a valuable asset for conducting research in the field of biometric recognition. The diversity of cattle in this context includes a range of ages, genders, breeds, and environmental conditions. Every photograph is taken from different quality cameras is thoroughly annotated, with special attention given to the muzzle of the cattle, which is considered an excellent biometric characteristic. In addition to its obvious practical benefits, this dataset possesses significant potential for extensive reuse. Within the domain of computer vision, it serves as a catalyst for algorithmic advancements, whereas in the agricultural sector, it augments practises related to cattle management. Machine learning aficionados highly value the use of machine learning for the construction and experimentation of models, especially in the context of transfer learning. Interdisciplinary collaboration is actively encouraged, facilitating the advancement of knowledge at the intersections of agriculture, computer science, and data science. The Cattle Biometrics Dataset represents a valuable resource that has the potential to stimulate significant advancements in various academic disciplines, fostering ground breaking research and innovation.
Details
- Language :
- English
- ISSN :
- 23523409
- Volume :
- 53
- Issue :
- 110125-
- Database :
- Directory of Open Access Journals
- Journal :
- Data in Brief
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b5410722f6e84b22805264c6ae250e0a
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.dib.2024.110125