A Portable Cattle Tagging Based on Muzzle Pattern

Indrabayu Indrabayu,Anugrayani Bustamin,Intan Sari Areni,Sri R.A Bugiwati,Ingrid Nurtanio,Muhammad Rahmatullah

doi:10.3991/ijim.v14i13.13237

Indrabayu Indrabayu, Anugrayani Bustamin + Show 4 more

Open Access

https://doi.org/10.3991/ijim.v14i13.13237

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Abstract

This research focuses on developing an Android-based cattle identification system that is applicable and easy to use. This system uses a Scale Invariant Feature Transform (SIFT) algorithm to extract features from the muzzle images, and Random Sample Consensus (RANSAC) algorithm to eliminate features incompatibility. The system is experimented with four threshold values, i.e. 10, 15, 20, and 30 using a total data of 460 muzzle images. In the first experiment, 3 images from each individual are used in the training stage and 2 images are used as the data test. In the second experiment, 5 images from each cattle are used in the training stage and 5 images are used as the data test. Data used in training stage are 244 images and in testing stage is 816 images. From the experiment, the highest accuracy rate is 98.1% with threshold values of 15 and 20. The execution time is also calculated to measure the processing time of the system. The average time taken to store an image to the database is 1.3 seconds. The main contribution of this research is technology implementation and more portable muzzle identification for local cattle in Makassar.

Highlights

Identifying livestock, especially cattle, is an important issue in maintaining livestock availability
The main aim of this research is to utilize the ability of Support Vector Machines (SVM) in pattern recognition and classification using Hamming Distance to improve the False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR)
Total images used in the training stage are 244 images, while 216 images are used as positive testing data

Summary

Introduction

Identifying livestock, especially cattle, is an important issue in maintaining livestock availability. Cattle identification is important for production management, disease management, legal ownership validation, and monitoring and tracking of livestock [2]. In 2016, Awad conducted research to look for biometric information from cattle muzzle. The results of his research prove that in addition to iris, cattle muzzle patterns become biometric features for animals such as cow and buffalo [16]. Hybrid feature extraction and recognition for the identification of cattle with muzzle pattern had been presented in 2016. The system used 5000 images of muzzle cattle with eight texture features for recognition. The result of this proposed by applying KNN and Fuzzy KNN classification technique is 94.5% and 96.74%, respectively. The system can provide better solutions to problems of registration, missed, swapped, false insurance claims, and traceability of cattle in the classical animal recognition-based methodologies [17]

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