Image-based Individual Cow Recognition using Body Patterns

Rotimi-Williams Bello,Daniel A,Abdullah Zawawi,Ahmad Sufril,Firstman Noah

doi:10.14569/ijacsa.2020.0110311

Rotimi-Williams Bello, Daniel A + Show 3 more

Open Access

https://doi.org/10.14569/ijacsa.2020.0110311

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Abstract

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.

Highlights

Cows in the past were classically monitored with the sole aim of aiding tracking, health information, performance recording, prevention against manipulation and swapping, and verification of false insurance claims
The patterns of the black and white body of the 10 species of cow were used for the calculation of the input parameters values for training. 400 images of body patterns (40 cows × 10 images of each subject) were used for the training of the proposed deep learning approach in the training phase. 600 pairs of testing (60 cows × 10 images of each subject) of the body patterns images in each fold were used for testing the probe images in the testing phase
Having tried out the effectuality of the proposed approach using images of cow’s body patterns for the recognition and identification of the cow, the comparison with other recognition algorithms is attained in order to evaluate the accuracy of the identification in proliferation settings

Summary

Introduction

Cows in the past were classically monitored with the sole aim of aiding tracking, health information, performance recording, prevention against manipulation and swapping, and verification of false insurance claims. Examples of the recognition technique that leaves a permanent mark are found in [1], [2], [3], [4], [5] with their drawbacks. The tattooing of ears, tagging of ears, microchips implant and branding are popular invasive identification techniques that leave a permanent mark on the animal’s body with so many challenges such as animal infections, mild sepsis, and hemorrhaging [2], [3]. Examples of the recognition technique that leaves a temporary mark on the body of the animal for identification purposes are found in the work of Barron et al [6] with their drawbacks. The classical methods of animal identification are not reliable; they are prone to fraudulent activities such as swapping, duplication and forgery of the so called unique identification numbers tagged on the animal’s body [7], [8], and cannot meet the required level expected from them for the monitoring and identification of animal [9]

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Results

Conclusion