Sheep Face Classification using Convolutional Neural Network

Bimantoro, Muhammad Zharfan and Emanuel, Andi Wahju Rahardjo (2021) Sheep Face Classification using Convolutional Neural Network. In: PROCEEDINGS OF 2021 13 INTERNATIONAL CONFERENCE ON INFORMATION & COMMUNICATION TECHNOLOGY AND SYSTEMS (ICTS). Institut Sains dan Teknologi Terpadu Surabaya (ISTTS), pp. 1-5. ISBN 978-1-6654-0514-0

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Text (Muhammad Zharfan Bimantoro and Andi Wahju Rahardjo Emanuel) 3. Sheep Face Classification using Convolutional Neural Network.pdf Download (2MB) | Preview

Abstract

Monitoring sheep species identification and classification in the farming environment can be a tedious task and can be a significant workload for a starting farmer. In this paper, Convolutional Neural Network is proposed to reduce the workload of sheep farmers. This experiment compares which neural architecture model is more useful to classify sheep species based on its face. The experiment was conducted using the training dataset obtained from Kaggle. The dataset contains 420 of each Marino sheep, Suffolk sheep, White Suffolk sheep, and Poll Dorset sheep, totaling 1680 sheep face images. This experiment was run on Google Colab, using the Resnet50 network architecture model and VGG16 network architecture model. The experiment shows good accuracy results on the dataset achieving 86% using the Resnet50 network architecture model. Better accuracy results were achieved using VGG16 network architecture, with an accuracy value of 94%.

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