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VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system
Cauliflower, a winter seasoned vegetable that originated in the Mediterranean region and arrived in Europe at the end of the 15th century, takes the lead in production among all vegetables. It's high in fiber and can keep us hydrated, and have medicinal properties like the chemical glucosinolat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256540/ https://www.ncbi.nlm.nih.gov/pubmed/35811654 http://dx.doi.org/10.1016/j.dib.2022.108422 |
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author | Sara, Umme Rajbongshi, Aditya Shakil, Rashiduzzaman Akter, Bonna Uddin, Mohammad Shorif |
author_facet | Sara, Umme Rajbongshi, Aditya Shakil, Rashiduzzaman Akter, Bonna Uddin, Mohammad Shorif |
author_sort | Sara, Umme |
collection | PubMed |
description | Cauliflower, a winter seasoned vegetable that originated in the Mediterranean region and arrived in Europe at the end of the 15th century, takes the lead in production among all vegetables. It's high in fiber and can keep us hydrated, and have medicinal properties like the chemical glucosinolates, which may help prevent cancer. If proper care is not given to the plants, several significant diseases can affect the plants, reducing production, quantity, and quality. Plant disease monitoring by hand is extremely tough because it demands a great deal of effort and time. Early detection of the diseases allows the agriculture sector to grow cauliflower more efficiently. In this scenario, an insightful and scientific dataset can be a lifesaver for researchers looking to analyze and observe different diseases in cauliflower development patterns. So, in this work, we present a well-organized and technically valuable dataset “VegNet’ to effectively recognize conditions in cauliflower plants and fruits. Healthy and disease-affected cauliflower head and leaves by black rot,downy mildew, and bacterial spot rot are included in our suggested dataset. The images were taken manually from December 20th to January 15th, when the flowers were fully blown, and most of the diseases were observed clearly. It is a well-organized dataset to develop and validate machine learning-based automated cauliflower disease detection algorithms. The dataset is hosted by the Institute – National Institute of Textile Engineering and Research (NITER),the Department of Computer Science and Engineering and is available at the link following: https://data.mendeley.com/datasets/t5sssfgn2v/3. |
format | Online Article Text |
id | pubmed-9256540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-92565402022-07-07 VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system Sara, Umme Rajbongshi, Aditya Shakil, Rashiduzzaman Akter, Bonna Uddin, Mohammad Shorif Data Brief Data Article Cauliflower, a winter seasoned vegetable that originated in the Mediterranean region and arrived in Europe at the end of the 15th century, takes the lead in production among all vegetables. It's high in fiber and can keep us hydrated, and have medicinal properties like the chemical glucosinolates, which may help prevent cancer. If proper care is not given to the plants, several significant diseases can affect the plants, reducing production, quantity, and quality. Plant disease monitoring by hand is extremely tough because it demands a great deal of effort and time. Early detection of the diseases allows the agriculture sector to grow cauliflower more efficiently. In this scenario, an insightful and scientific dataset can be a lifesaver for researchers looking to analyze and observe different diseases in cauliflower development patterns. So, in this work, we present a well-organized and technically valuable dataset “VegNet’ to effectively recognize conditions in cauliflower plants and fruits. Healthy and disease-affected cauliflower head and leaves by black rot,downy mildew, and bacterial spot rot are included in our suggested dataset. The images were taken manually from December 20th to January 15th, when the flowers were fully blown, and most of the diseases were observed clearly. It is a well-organized dataset to develop and validate machine learning-based automated cauliflower disease detection algorithms. The dataset is hosted by the Institute – National Institute of Textile Engineering and Research (NITER),the Department of Computer Science and Engineering and is available at the link following: https://data.mendeley.com/datasets/t5sssfgn2v/3. Elsevier 2022-06-26 /pmc/articles/PMC9256540/ /pubmed/35811654 http://dx.doi.org/10.1016/j.dib.2022.108422 Text en © 2022 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Sara, Umme Rajbongshi, Aditya Shakil, Rashiduzzaman Akter, Bonna Uddin, Mohammad Shorif VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system |
title | VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system |
title_full | VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system |
title_fullStr | VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system |
title_full_unstemmed | VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system |
title_short | VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system |
title_sort | vegnet: an organized dataset of cauliflower disease for a sustainable agro-based automation system |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256540/ https://www.ncbi.nlm.nih.gov/pubmed/35811654 http://dx.doi.org/10.1016/j.dib.2022.108422 |
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