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Banana Plant Disease Classification Using Hybrid Convolutional Neural Network

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers....

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Autores principales: Narayanan, K. Lakshmi, Krishnan, R. Santhana, Robinson, Y. Harold, Julie, E. Golden, Vimal, S., Saravanan, V., Kaliappan, M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890843/
https://www.ncbi.nlm.nih.gov/pubmed/35251158
http://dx.doi.org/10.1155/2022/9153699
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author Narayanan, K. Lakshmi
Krishnan, R. Santhana
Robinson, Y. Harold
Julie, E. Golden
Vimal, S.
Saravanan, V.
Kaliappan, M.
author_facet Narayanan, K. Lakshmi
Krishnan, R. Santhana
Robinson, Y. Harold
Julie, E. Golden
Vimal, S.
Saravanan, V.
Kaliappan, M.
author_sort Narayanan, K. Lakshmi
collection PubMed
description Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.
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spelling pubmed-88908432022-03-03 Banana Plant Disease Classification Using Hybrid Convolutional Neural Network Narayanan, K. Lakshmi Krishnan, R. Santhana Robinson, Y. Harold Julie, E. Golden Vimal, S. Saravanan, V. Kaliappan, M. Comput Intell Neurosci Research Article Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques. Hindawi 2022-02-23 /pmc/articles/PMC8890843/ /pubmed/35251158 http://dx.doi.org/10.1155/2022/9153699 Text en Copyright © 2022 K. Lakshmi Narayanan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Narayanan, K. Lakshmi
Krishnan, R. Santhana
Robinson, Y. Harold
Julie, E. Golden
Vimal, S.
Saravanan, V.
Kaliappan, M.
Banana Plant Disease Classification Using Hybrid Convolutional Neural Network
title Banana Plant Disease Classification Using Hybrid Convolutional Neural Network
title_full Banana Plant Disease Classification Using Hybrid Convolutional Neural Network
title_fullStr Banana Plant Disease Classification Using Hybrid Convolutional Neural Network
title_full_unstemmed Banana Plant Disease Classification Using Hybrid Convolutional Neural Network
title_short Banana Plant Disease Classification Using Hybrid Convolutional Neural Network
title_sort banana plant disease classification using hybrid convolutional neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890843/
https://www.ncbi.nlm.nih.gov/pubmed/35251158
http://dx.doi.org/10.1155/2022/9153699
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