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Classification of date fruits in a controlled environment using Convolutional Neural Networks

Alhamdan, W. and Howe, J. M. ORCID: 0000-0001-8013-6941 (2020). Classification of date fruits in a controlled environment using Convolutional Neural Networks. Advances in Intelligent Systems and Computing,

Abstract

This paper explores the use of Convolutional Neural Networks in classifying images of date fruits as one of 9 varieties, creating several models with the highest achieving 97% accuracy. It contributes an original dataset of 1658 high-quality images taken in a controlled environment for use in both the computer vision and agricultural technology fields. A range of models is explored and trained, both with and without data augmentation, leading to high classification accuracy.

Publication Type: Article
Additional Information: This is a pre-print of an article to be published in Advances in Intelligent Systems and Computing. The final authenticated version will be available online at: http://www.springer.com/series/11156?detailsPage=titles
Publisher Keywords: Convolutional Neural Networks, supervised learning, classification, date fruit
Subjects: Q Science > QK Botany
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date Deposited: 28 Oct 2020 11:37
URI: https://openaccess.city.ac.uk/id/eprint/25158
[img] Text - Accepted Version
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