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Dataset of cattle biometrics through muzzle images

Authors :
Syed Umaid Ahmed
Jaroslav Frnda
Muhammad Waqas
Muhammad Hassan Khan
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