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A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize
Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and overall crop yield. Therefore, it is quite essentia...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9833299/ https://www.ncbi.nlm.nih.gov/pubmed/36643296 http://dx.doi.org/10.3389/fpls.2022.1077568 |
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author | Haque, Md. Ashraful Marwaha, Sudeep Arora, Alka Deb, Chandan Kumar Misra, Tanuj Nigam, Sapna Hooda, Karambir Singh |
author_facet | Haque, Md. Ashraful Marwaha, Sudeep Arora, Alka Deb, Chandan Kumar Misra, Tanuj Nigam, Sapna Hooda, Karambir Singh |
author_sort | Haque, Md. Ashraful |
collection | PubMed |
description | Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and overall crop yield. Therefore, it is quite essential to identify the disease at the earliest possible stage to overcome the yield loss. In this study, we created an image database of maize crop, MDSD (Maydis leaf blight Disease Severity Dataset), containing 1,760 digital images of MLB disease, collected from different agricultural fields and categorized into four groups viz. healthy, low, medium and high severity stages. Next, we proposed a lightweight convolutional neural network (CNN) to identify the severity stages of MLB disease. The proposed network is a simple CNN framework augmented with two modified Inception modules, making it a lightweight and efficient multi-scale feature extractor. The proposed network reported approx. 99.13% classification accuracy with the f1-score of 98.97% on the test images of MDSD. Furthermore, the class-wise accuracy levels were 100% for healthy samples, 98% for low severity samples and 99% for the medium and high severity samples. In addition to that, our network significantly outperforms the popular pretrained models, viz. VGG16, VGG19, InceptionV3, ResNet50, Xception, MobileNetV2, DenseNet121 and NASNetMobile for the MDSD image database. The experimental findings revealed that our proposed lightweight network is excellent in identifying the images of severity stages of MLB disease despite complicated background conditions. |
format | Online Article Text |
id | pubmed-9833299 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98332992023-01-12 A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize Haque, Md. Ashraful Marwaha, Sudeep Arora, Alka Deb, Chandan Kumar Misra, Tanuj Nigam, Sapna Hooda, Karambir Singh Front Plant Sci Plant Science Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and overall crop yield. Therefore, it is quite essential to identify the disease at the earliest possible stage to overcome the yield loss. In this study, we created an image database of maize crop, MDSD (Maydis leaf blight Disease Severity Dataset), containing 1,760 digital images of MLB disease, collected from different agricultural fields and categorized into four groups viz. healthy, low, medium and high severity stages. Next, we proposed a lightweight convolutional neural network (CNN) to identify the severity stages of MLB disease. The proposed network is a simple CNN framework augmented with two modified Inception modules, making it a lightweight and efficient multi-scale feature extractor. The proposed network reported approx. 99.13% classification accuracy with the f1-score of 98.97% on the test images of MDSD. Furthermore, the class-wise accuracy levels were 100% for healthy samples, 98% for low severity samples and 99% for the medium and high severity samples. In addition to that, our network significantly outperforms the popular pretrained models, viz. VGG16, VGG19, InceptionV3, ResNet50, Xception, MobileNetV2, DenseNet121 and NASNetMobile for the MDSD image database. The experimental findings revealed that our proposed lightweight network is excellent in identifying the images of severity stages of MLB disease despite complicated background conditions. Frontiers Media S.A. 2022-12-19 /pmc/articles/PMC9833299/ /pubmed/36643296 http://dx.doi.org/10.3389/fpls.2022.1077568 Text en Copyright © 2022 Haque, Marwaha, Arora, Deb, Misra, Nigam and Hooda 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 Haque, Md. Ashraful Marwaha, Sudeep Arora, Alka Deb, Chandan Kumar Misra, Tanuj Nigam, Sapna Hooda, Karambir Singh A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize |
title | A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize |
title_full | A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize |
title_fullStr | A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize |
title_full_unstemmed | A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize |
title_short | A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize |
title_sort | lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9833299/ https://www.ncbi.nlm.nih.gov/pubmed/36643296 http://dx.doi.org/10.3389/fpls.2022.1077568 |
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