DenseNet-201 and Xception pre-trained deep learning models for fruit recognition

Salim, F, Saeed, F, Basurra, S, Qasem, SN and Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X, 2023. DenseNet-201 and Xception pre-trained deep learning models for fruit recognition. Electronics, 12 (14): 3132. ISSN 2079-9292

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Abstract

With the dramatic increase of the global population and with food insecurity increasing, it has become a major concern for both individuals and governments to fulfill the need for foods such as vegetables and fruits. Moreover, the desire for the consumption of healthy food, including fruit, has increased the need for applications in the field of agriculture that help to achieve better methods for fruit sorting and fruit disease prediction and classification. Automated fruit recognition is a potential solution to reduce the time and labor required to identify different fruits in situations such as retail stores during checkout, fruit processing centers during sorting, and orchards during harvest. Automating these processes reduces the need for human intervention, making them cheaper, faster, and immune to human error and biases. Past research in the field has focused mainly on the size, shape, and color features of fruits or employed convolutional neural networks (CNNs) for their classification. This study investigates the effectiveness of pre-trained deep learning models for fruit classification using two distinct datasets: Fruits-360 and the Fruit Recognition dataset. Four pre-trained models, DenseNet-201, Xception, MobileNetV3-Small, and ResNet-50, were chosen for the experiments based on their architecture and features. The results show that all models achieved almost 99% accuracy or higher with Fruits-360. With the Fruit Recognition dataset, DenseNet-201 and Xception achieved accuracies of around 98%. The good results exhibited by DenseNet-201 and Xception on both the datasets are remarkable, with DenseNet-201 attaining accuracies of 99.87% and 98.94%, and Xception attaining 99.13% and 97.73% accuracy, respectively, on Fruits-360 and the Fruit Recognition dataset.

Item Type:	Journal article
Publication Title:	Electronics
Creators:	Salim, F., Saeed, F., Basurra, S., Qasem, S.N. and Al-Hadhrami, T.
Publisher:	MDPI
Date:	19 July 2023
Volume:	12
Number:	14
ISSN:	2079-9292
Identifiers:	Number Type 10.3390/electronics12143132 DOI 1784681 Other
Rights:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	24 Jul 2023 11:58
Last Modified:	24 Jul 2023 11:58
URI:	https://irep.ntu.ac.uk/id/eprint/49426

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