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 |
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