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Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainabilit...

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Autores principales: Kaur, Prabhjot, Harnal, Shilpi, Tiwari, Rajeev, Upadhyay, Shuchi, Bhatia, Surbhi, Mashat, Arwa, Alabdali, Aliaa M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8779777/
https://www.ncbi.nlm.nih.gov/pubmed/35062534
http://dx.doi.org/10.3390/s22020575
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author Kaur, Prabhjot
Harnal, Shilpi
Tiwari, Rajeev
Upadhyay, Shuchi
Bhatia, Surbhi
Mashat, Arwa
Alabdali, Aliaa M.
author_facet Kaur, Prabhjot
Harnal, Shilpi
Tiwari, Rajeev
Upadhyay, Shuchi
Bhatia, Surbhi
Mashat, Arwa
Alabdali, Aliaa M.
author_sort Kaur, Prabhjot
collection PubMed
description Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.
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spelling pubmed-87797772022-01-22 Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction Kaur, Prabhjot Harnal, Shilpi Tiwari, Rajeev Upadhyay, Shuchi Bhatia, Surbhi Mashat, Arwa Alabdali, Aliaa M. Sensors (Basel) Article Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures. MDPI 2022-01-12 /pmc/articles/PMC8779777/ /pubmed/35062534 http://dx.doi.org/10.3390/s22020575 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kaur, Prabhjot
Harnal, Shilpi
Tiwari, Rajeev
Upadhyay, Shuchi
Bhatia, Surbhi
Mashat, Arwa
Alabdali, Aliaa M.
Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction
title Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction
title_full Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction
title_fullStr Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction
title_full_unstemmed Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction
title_short Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction
title_sort recognition of leaf disease using hybrid convolutional neural network by applying feature reduction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8779777/
https://www.ncbi.nlm.nih.gov/pubmed/35062534
http://dx.doi.org/10.3390/s22020575
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