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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10422257 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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