The existence of illumination variation, non-rigid object, occlusion, non-linear motion, and real-time implementation requirement has made tracking in computer vision a challenging task. In order to recognize individual cow and to mitigate all the challenging tasks, an image processing system is proposed using the body pattern images of the cow. This system accepts an input image, performs processing operation on the image, and output results in form of classification under certain categories. Technically, convolutional neural network is modeled for the training and testing of each pattern image of 1000 acquired images of 10 species of cow which will pass it through a series of convolution layers with filters, pooling, fully connected layers and softmax function for the pattern images classification with probabilistic values between 0 and 1. The performance evaluation of the proposed system for both training and testing data was carried out for each cow's identification and 92.59% and 89.95% accuracies were achieved respectively.

Petersen was the first published paper to address cattle biometrics and identification problem by suggesting a permanent cattle identification method based on nose print principles widely accepted today. His major concern was on proper identification of cattle for registration and of cattle on an official test so that the possibility of swapping, false insurance claims, and ownership disputes can be guarded against. It was with this identification problem in the mind of every breeder that the practicable suggestion of using nose print as means of identification was made by O. H. Baker of the American Jersey Cattle Club in Petersen’s paper entitled “The identification of the bovine by means of nose-prints”. Before the advent of the nose print method, cattle identification has been by conventional constructs such as tattoo, tags, photographs, descriptions, branding (hot and freeze), ear notching, and sketching (drawings) the color markings on them on paper for registration and identification purposes. These classical methods of identification cause trouble among the breeders especially when their cattle are sold or are on an official test due to lack of artistic ability on the part of the breeders which makes the matching of the sketches and the markings on the cattle disagree. Presented in this paper are the various cattle biometrics and identification methods, most especially from the classical methods to the modern methods.

The productivity of livestock farming depends on the welfare of the livestock. This can be achieved by physically and constantly monitoring their behaviors and activities by human experts. However, the degree of having high accuracy and consistency with manual monitoring in a commercial farm is herculean, and in most cases impractical. Hence, there is a need for a method that can overcome the challenges. Proposed in this paper, therefore, is the cow detection and monitoring method using computer vision techniques. The proposed method is capable of tracking and identifying cow objects in video experiments, thereby actualizing precision livestock farming. The method generates reasonable results when compared to other methods.