Sheep Identification Using a Hybrid Deep Learning and Bayesian Optimization Approach

Abstract

Sheep are considered a necessary source of food production worldwide. Therefore, the sheep identification is vital for managing breeding and disease. Moreover, it is the only guarantee of an individual's ownership. Therefore, in this paper, sheep identities were recognized by a deep convolutional neural network using facial bio-metrics. To obtain the best possible accuracy, different neural networks designs were surveyed and tested in this paper. The Bayesian optimization was used to automatically set the parameters for a convolutional neural network; in addition, the AlexNet configuration was also examined in this paper. In this paper, the sheep recognition algorithms were tested on a data set of 52 sheep. Not more than 10 images were taken of each sheep in different postures. Thus, the data augmentation methodologies such as rotation, reflection, scaling, blurring, and brightness modification were applied; 1000 images of each sheep were obtained for training and validation. The experiments conducted in this paper achieved an accuracy of 98%. Our approach outperforms previous approaches for sheep identification.