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Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat var...

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Autores principales: Mi, Zhiwen, Zhang, Xudong, Su, Jinya, Han, Dejun, Su, Baofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509068/
https://www.ncbi.nlm.nih.gov/pubmed/33013976
http://dx.doi.org/10.3389/fpls.2020.558126
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author Mi, Zhiwen
Zhang, Xudong
Su, Jinya
Han, Dejun
Su, Baofeng
author_facet Mi, Zhiwen
Zhang, Xudong
Su, Jinya
Han, Dejun
Su, Baofeng
author_sort Mi, Zhiwen
collection PubMed
description Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions.
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spelling pubmed-75090682020-10-02 Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices Mi, Zhiwen Zhang, Xudong Su, Jinya Han, Dejun Su, Baofeng Front Plant Sci Plant Science Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions. Frontiers Media S.A. 2020-09-09 /pmc/articles/PMC7509068/ /pubmed/33013976 http://dx.doi.org/10.3389/fpls.2020.558126 Text en Copyright © 2020 Mi, Zhang, Su, Han and Su http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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.
spellingShingle Plant Science
Mi, Zhiwen
Zhang, Xudong
Su, Jinya
Han, Dejun
Su, Baofeng
Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices
title Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices
title_full Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices
title_fullStr Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices
title_full_unstemmed Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices
title_short Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices
title_sort wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509068/
https://www.ncbi.nlm.nih.gov/pubmed/33013976
http://dx.doi.org/10.3389/fpls.2020.558126
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