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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,


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:
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
[img] Text - Accepted Version
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