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Deep learning-based approach for identification of diseases of maize crop

In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from e...

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Autores principales: Haque, Md. Ashraful, Marwaha, Sudeep, Deb, Chandan Kumar, Nigam, Sapna, Arora, Alka, Hooda, Karambir Singh, Soujanya, P. Lakshmi, Aggarwal, Sumit Kumar, Lall, Brejesh, Kumar, Mukesh, Islam, Shahnawazul, Panwar, Mohit, Kumar, Prabhat, Agrawal, R. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012772/
https://www.ncbi.nlm.nih.gov/pubmed/35428845
http://dx.doi.org/10.1038/s41598-022-10140-z
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author Haque, Md. Ashraful
Marwaha, Sudeep
Deb, Chandan Kumar
Nigam, Sapna
Arora, Alka
Hooda, Karambir Singh
Soujanya, P. Lakshmi
Aggarwal, Sumit Kumar
Lall, Brejesh
Kumar, Mukesh
Islam, Shahnawazul
Panwar, Mohit
Kumar, Prabhat
Agrawal, R. C.
author_facet Haque, Md. Ashraful
Marwaha, Sudeep
Deb, Chandan Kumar
Nigam, Sapna
Arora, Alka
Hooda, Karambir Singh
Soujanya, P. Lakshmi
Aggarwal, Sumit Kumar
Lall, Brejesh
Kumar, Mukesh
Islam, Shahnawazul
Panwar, Mohit
Kumar, Prabhat
Agrawal, R. C.
author_sort Haque, Md. Ashraful
collection PubMed
description In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of ‘Inception-v3’ network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.
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spelling pubmed-90127722022-04-18 Deep learning-based approach for identification of diseases of maize crop Haque, Md. Ashraful Marwaha, Sudeep Deb, Chandan Kumar Nigam, Sapna Arora, Alka Hooda, Karambir Singh Soujanya, P. Lakshmi Aggarwal, Sumit Kumar Lall, Brejesh Kumar, Mukesh Islam, Shahnawazul Panwar, Mohit Kumar, Prabhat Agrawal, R. C. Sci Rep Article In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of ‘Inception-v3’ network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds. Nature Publishing Group UK 2022-04-15 /pmc/articles/PMC9012772/ /pubmed/35428845 http://dx.doi.org/10.1038/s41598-022-10140-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Haque, Md. Ashraful
Marwaha, Sudeep
Deb, Chandan Kumar
Nigam, Sapna
Arora, Alka
Hooda, Karambir Singh
Soujanya, P. Lakshmi
Aggarwal, Sumit Kumar
Lall, Brejesh
Kumar, Mukesh
Islam, Shahnawazul
Panwar, Mohit
Kumar, Prabhat
Agrawal, R. C.
Deep learning-based approach for identification of diseases of maize crop
title Deep learning-based approach for identification of diseases of maize crop
title_full Deep learning-based approach for identification of diseases of maize crop
title_fullStr Deep learning-based approach for identification of diseases of maize crop
title_full_unstemmed Deep learning-based approach for identification of diseases of maize crop
title_short Deep learning-based approach for identification of diseases of maize crop
title_sort deep learning-based approach for identification of diseases of maize crop
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012772/
https://www.ncbi.nlm.nih.gov/pubmed/35428845
http://dx.doi.org/10.1038/s41598-022-10140-z
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