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Automatic Diagnosis of Rice Diseases Using Deep Learning

Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic dia...

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Autores principales: Deng, Ruoling, Tao, Ming, Xing, Hang, Yang, Xiuli, Liu, Chuang, Liao, Kaifeng, Qi, Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416767/
https://www.ncbi.nlm.nih.gov/pubmed/34490004
http://dx.doi.org/10.3389/fpls.2021.701038
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author Deng, Ruoling
Tao, Ming
Xing, Hang
Yang, Xiuli
Liu, Chuang
Liao, Kaifeng
Qi, Long
author_facet Deng, Ruoling
Tao, Ming
Xing, Hang
Yang, Xiuli
Liu, Chuang
Liao, Kaifeng
Qi, Long
author_sort Deng, Ruoling
collection PubMed
description Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic diagnosis method was developed and implemented in a smartphone app. The method was developed using deep learning based on a large dataset that contained 33,026 images of six types of rice diseases: leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. The core of the method was the Ensemble Model in which submodels were integrated. Finally, the Ensemble Model was validated using a separate set of images. Results showed that the three best submodels were DenseNet-121, SE-ResNet-50, and ResNeSt-50, in terms of several attributes, such as, learning rate, precision, recall, and disease recognition accuracy. Therefore, these three submodels were selected and integrated in the Ensemble Model. The Ensemble Model minimized confusion among the different types of disease, reducing misdiagnosis of the disease. Using the Ensemble Model to diagnose six types of rice diseases, an overall accuracy of 91% was achieved, which is considered to be reasonably good, considering the appearance similarities in some types of rice disease. The smartphone app allowed the client to use the Ensemble Model on the web server through a network, which was convenient and efficient for the field diagnosis of rice leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot.
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spelling pubmed-84167672021-09-05 Automatic Diagnosis of Rice Diseases Using Deep Learning Deng, Ruoling Tao, Ming Xing, Hang Yang, Xiuli Liu, Chuang Liao, Kaifeng Qi, Long Front Plant Sci Plant Science Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic diagnosis method was developed and implemented in a smartphone app. The method was developed using deep learning based on a large dataset that contained 33,026 images of six types of rice diseases: leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. The core of the method was the Ensemble Model in which submodels were integrated. Finally, the Ensemble Model was validated using a separate set of images. Results showed that the three best submodels were DenseNet-121, SE-ResNet-50, and ResNeSt-50, in terms of several attributes, such as, learning rate, precision, recall, and disease recognition accuracy. Therefore, these three submodels were selected and integrated in the Ensemble Model. The Ensemble Model minimized confusion among the different types of disease, reducing misdiagnosis of the disease. Using the Ensemble Model to diagnose six types of rice diseases, an overall accuracy of 91% was achieved, which is considered to be reasonably good, considering the appearance similarities in some types of rice disease. The smartphone app allowed the client to use the Ensemble Model on the web server through a network, which was convenient and efficient for the field diagnosis of rice leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. Frontiers Media S.A. 2021-08-19 /pmc/articles/PMC8416767/ /pubmed/34490004 http://dx.doi.org/10.3389/fpls.2021.701038 Text en Copyright © 2021 Deng, Tao, Xing, Yang, Liu, Liao and Qi. https://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
Deng, Ruoling
Tao, Ming
Xing, Hang
Yang, Xiuli
Liu, Chuang
Liao, Kaifeng
Qi, Long
Automatic Diagnosis of Rice Diseases Using Deep Learning
title Automatic Diagnosis of Rice Diseases Using Deep Learning
title_full Automatic Diagnosis of Rice Diseases Using Deep Learning
title_fullStr Automatic Diagnosis of Rice Diseases Using Deep Learning
title_full_unstemmed Automatic Diagnosis of Rice Diseases Using Deep Learning
title_short Automatic Diagnosis of Rice Diseases Using Deep Learning
title_sort automatic diagnosis of rice diseases using deep learning
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416767/
https://www.ncbi.nlm.nih.gov/pubmed/34490004
http://dx.doi.org/10.3389/fpls.2021.701038
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