Cattle Identification Using Muzzle Images and Deep Learning Techniques
G. N. Kimani P. Oluwadara P. Fashingabo M. Busogi E. Luhanga K. Sowon L. Chacha
Traditional animal identification methods such as ear-tagging, ear notching,
and branding have been effective but pose risks to the animal and have
scalability issues. Electrical methods offer better tracking and monitoring but
require specialized equipment and are susceptible to attacks. Biometric
identification using time-immutable dermatoglyphic features such as muzzle
prints and iris patterns is a promising solution. This project explores cattle
identification using 4923 muzzle images collected from 268 beef cattle. Two
deep learning classification models are implemented - wide ResNet50 and
VGG16\_BN and image compression is done to lower the image quality and adapt
the models to work for the African context. From the experiments run, a maximum
accuracy of 99.5\% is achieved while using the wide ResNet50 model with a
compression retaining 25\% of the original image. From the study, it is noted
that the time required by the models to train and converge as well as
recognition time are dependent on the machine used to run the model.