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

Alhamdan, W. & Howe, J. M. ORCID: 0000-0001-8013-6941 (2021). Classification of date fruits in a controlled environment using Convolutional Neural Networks. In: Hassanien, A. E., Chang, K. C. & Mincong, T. (Eds.), AMLTA 2021: Advanced Machine Learning Technologies and Applications. (pp. 154-163). Cham, Switzerland: Springer. ISBN 978-3-030-69716-7 doi: 10.1007/978-3-030-69717-4_16

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