A Portable Cattle Tagging Based on Muzzle Pattern

A Portable Cattle Tagging Based on Muzzle Pattern

cattle identification

SIFT

RANSAC

muzzle pattern

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.

Indrabayu, Indrabayu

Nurtanio, Ingrid

Areni, Intan Sari

Bugiwati, Sri R.A

Bustamin, Anugrayani

Rahmatullah, Muhammad

International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 13 (2020); pp. 134-149

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2020-08-14

Copyright (c) 2020 Indrabayu Indrabayu, Ingrid Nurtanio, Intan Sari Areni, Sri R.A Bugiwati, Anugrayani Bustamin, Muhammad Rahmatullah

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article