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Attention-Based Recurrent Neural Network for Plant Disease Classification

Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to ou...

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Autores principales: Lee, Sue Han, Goëau, Hervé, Bonnet, Pierre, Joly, Alexis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767846/
https://www.ncbi.nlm.nih.gov/pubmed/33381135
http://dx.doi.org/10.3389/fpls.2020.601250
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author Lee, Sue Han
Goëau, Hervé
Bonnet, Pierre
Joly, Alexis
author_facet Lee, Sue Han
Goëau, Hervé
Bonnet, Pierre
Joly, Alexis
author_sort Lee, Sue Han
collection PubMed
description Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.
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spelling pubmed-77678462020-12-29 Attention-Based Recurrent Neural Network for Plant Disease Classification Lee, Sue Han Goëau, Hervé Bonnet, Pierre Joly, Alexis Front Plant Sci Plant Science Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future. Frontiers Media S.A. 2020-12-14 /pmc/articles/PMC7767846/ /pubmed/33381135 http://dx.doi.org/10.3389/fpls.2020.601250 Text en Copyright © 2020 Lee, Goëau, Bonnet and Joly. 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
Lee, Sue Han
Goëau, Hervé
Bonnet, Pierre
Joly, Alexis
Attention-Based Recurrent Neural Network for Plant Disease Classification
title Attention-Based Recurrent Neural Network for Plant Disease Classification
title_full Attention-Based Recurrent Neural Network for Plant Disease Classification
title_fullStr Attention-Based Recurrent Neural Network for Plant Disease Classification
title_full_unstemmed Attention-Based Recurrent Neural Network for Plant Disease Classification
title_short Attention-Based Recurrent Neural Network for Plant Disease Classification
title_sort attention-based recurrent neural network for plant disease classification
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767846/
https://www.ncbi.nlm.nih.gov/pubmed/33381135
http://dx.doi.org/10.3389/fpls.2020.601250
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