City Research Online

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:	Conference or Workshop Item (Paper)
Additional Information:	This is a post-peer-review, pre-copyedit version of chapter published in AMLTA 2021: Advanced Machine Learning Technologies and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-69717-4_16
Publisher Keywords:	Convolutional Neural Networks, supervised learning, classification, date fruit
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science 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 Science & Technology > Computer Science

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