Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106086
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Design | en_US |
dc.creator | Zhong, Y | en_US |
dc.creator | Teng, ZH | en_US |
dc.creator | Tong, MJ | en_US |
dc.date.accessioned | 2024-05-03T00:45:07Z | - |
dc.date.available | 2024-05-03T00:45:07Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106086 | - |
dc.language.iso | en | en_US |
dc.publisher | Frontiers Research Foundation | en_US |
dc.rights | © 2023 Zhong, Teng and Tong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | en_US |
dc.rights | The following publication Zhong Y, Teng Z and Tong M (2023) LightMixer: A novel lightweight convolutional neural network for tomato disease detection. Front. Plant Sci. 14:1166296 is available at https://dx.doi.org/10.3389/fpls.2023.1166296. | en_US |
dc.subject | Tomato leaf disease | en_US |
dc.subject | Lightweight model | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Disease detection | en_US |
dc.title | LightMixer : a novel lightweight convolutional neural network for tomato disease detection | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 14 | en_US |
dc.identifier.doi | 10.3389/fpls.2023.1166296 | en_US |
dcterms.abstract | Tomatoes are among the very important crops grown worldwide. However, tomato diseases can harm the health of tomato plants during growth and reduce tomato yields over large areas. The development of computer vision technology offers the prospect of solving this problem. However, traditional deep learning algorithms require a high computational cost and several parameters. Therefore, a lightweight tomato leaf disease identification model called LightMixer was designed in this study. The LightMixer model comprises a depth convolution with a Phish module and a light residual module. Depth convolution with the Phish module represents a lightweight convolution module designed to splice nonlinear activation functions with depth convolution as the backbone; it also focuses on lightweight convolutional feature extraction to facilitate deep feature fusion. The light residual module was built based on lightweight residual blocks to accelerate the computational efficiency of the entire network architecture and reduce the information loss of disease features. Experimental results show that the proposed LightMixer model achieved 99.3% accuracy on public datasets while requiring only 1.5 M parameters, an improvement over other classical convolutional neural network and lightweight models, and can be used for automatic tomato leaf disease identification on mobile devices. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Frontiers in plant science, 2023, v. 14, 1166296 | en_US |
dcterms.isPartOf | Frontiers in plant science | en_US |
dcterms.issued | 2023 | - |
dc.identifier.isi | WOS:000991412200001 | - |
dc.identifier.eissn | 1664-462X | en_US |
dc.identifier.artn | 1166296 | en_US |
dc.description.validate | 202405 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
fpls-14-1166296.pdf | 5.96 MB | Adobe PDF | View/Open |
Page views
15
Citations as of Jun 30, 2024
Downloads
2
Citations as of Jun 30, 2024
SCOPUSTM
Citations
7
Citations as of Jun 21, 2024
WEB OF SCIENCETM
Citations
4
Citations as of Jul 4, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.