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Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers

In order for a country’s economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming...

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Autores principales: Parez, Sana, Dilshad, Naqqash, Alghamdi, Norah Saleh, Alanazi, Turki M., Lee, Jong Weon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422257/
https://www.ncbi.nlm.nih.gov/pubmed/37571732
http://dx.doi.org/10.3390/s23156949
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author Parez, Sana
Dilshad, Naqqash
Alghamdi, Norah Saleh
Alanazi, Turki M.
Lee, Jong Weon
author_facet Parez, Sana
Dilshad, Naqqash
Alghamdi, Norah Saleh
Alanazi, Turki M.
Lee, Jong Weon
author_sort Parez, Sana
collection PubMed
description In order for a country’s economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming. This leads to an agricultural management system in need of a method for automatically detecting disease at an early stage. As a consequence of dimensionality reduction, CNN-based models use pooling layers, which results in the loss of vital information, including the precise location of the most prominent features. In response to these challenges, we propose a fine-tuned technique, GreenViT, for detecting plant infections and diseases based on Vision Transformers (ViTs). Similar to word embedding, we divide the input image into smaller blocks or patches and feed these to the ViT sequentially. Our approach leverages the strengths of ViTs in order to overcome the problems associated with CNN-based models. Experiments on widely used benchmark datasets were conducted to evaluate the proposed GreenViT performance. Based on the obtained experimental outcomes, the proposed technique outperforms state-of-the-art (SOTA) CNN models for detecting plant diseases.
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spelling pubmed-104222572023-08-13 Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers Parez, Sana Dilshad, Naqqash Alghamdi, Norah Saleh Alanazi, Turki M. Lee, Jong Weon Sensors (Basel) Article In order for a country’s economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming. This leads to an agricultural management system in need of a method for automatically detecting disease at an early stage. As a consequence of dimensionality reduction, CNN-based models use pooling layers, which results in the loss of vital information, including the precise location of the most prominent features. In response to these challenges, we propose a fine-tuned technique, GreenViT, for detecting plant infections and diseases based on Vision Transformers (ViTs). Similar to word embedding, we divide the input image into smaller blocks or patches and feed these to the ViT sequentially. Our approach leverages the strengths of ViTs in order to overcome the problems associated with CNN-based models. Experiments on widely used benchmark datasets were conducted to evaluate the proposed GreenViT performance. Based on the obtained experimental outcomes, the proposed technique outperforms state-of-the-art (SOTA) CNN models for detecting plant diseases. MDPI 2023-08-04 /pmc/articles/PMC10422257/ /pubmed/37571732 http://dx.doi.org/10.3390/s23156949 Text en © 2023 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
Parez, Sana
Dilshad, Naqqash
Alghamdi, Norah Saleh
Alanazi, Turki M.
Lee, Jong Weon
Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers
title Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers
title_full Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers
title_fullStr Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers
title_full_unstemmed Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers
title_short Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers
title_sort visual intelligence in precision agriculture: exploring plant disease detection via efficient vision transformers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422257/
https://www.ncbi.nlm.nih.gov/pubmed/37571732
http://dx.doi.org/10.3390/s23156949
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