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Fruit classification using attention-based MobileNetV2 for industrial applications

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posted on 2024-07-23, 03:43 authored by Tej ShahiTej Shahi, C Sitaula, Arjun NeupaneArjun Neupane, Wanwu GuoWanwu Guo
Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future.

History

Volume

17

Issue

2

Start Page

1

End Page

21

Number of Pages

21

eISSN

1932-6203

ISSN

1932-6203

Publisher

Public Library of Science (PLoS)

Publisher License

CC BY

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2022-02-13

Era Eligible

  • Yes

Medium

Electronic-eCollection

Journal

PLoS ONE

Article Number

e0264586

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