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Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification
This article presents a dataset of insect-damaged soybean leaves. The capture of images was carried out on several soy farms, under realistic weather conditions, using two cell phones and a UAV. The dataset consists of 3 (three) folders with a total of 6,410 images. The dataset is divided into three...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741414/ https://www.ncbi.nlm.nih.gov/pubmed/35028343 http://dx.doi.org/10.1016/j.dib.2021.107756 |
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author | Mignoni, Maria Eloisa Honorato, Aislan Kunst, Rafael Righi, Rodrigo Massuquetti, Angélica |
author_facet | Mignoni, Maria Eloisa Honorato, Aislan Kunst, Rafael Righi, Rodrigo Massuquetti, Angélica |
author_sort | Mignoni, Maria Eloisa |
collection | PubMed |
description | This article presents a dataset of insect-damaged soybean leaves. The capture of images was carried out on several soy farms, under realistic weather conditions, using two cell phones and a UAV. The dataset consists of 3 (three) folders with a total of 6,410 images. The dataset is divided into three categories: (I) healthy plants, (II) plants affected by caterpillars, and (III) images of plants damaged by Diabrotica speciosa. This dataset allows training and validation of machine learning models to diagnose, recognize, and classify soybeans affected by caterpillars or Diabrotica speciosa. The images can be processed according to the user’s need since only the size was standardized during the pre-processing phase. |
format | Online Article Text |
id | pubmed-8741414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-87414142022-01-12 Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification Mignoni, Maria Eloisa Honorato, Aislan Kunst, Rafael Righi, Rodrigo Massuquetti, Angélica Data Brief Data Article This article presents a dataset of insect-damaged soybean leaves. The capture of images was carried out on several soy farms, under realistic weather conditions, using two cell phones and a UAV. The dataset consists of 3 (three) folders with a total of 6,410 images. The dataset is divided into three categories: (I) healthy plants, (II) plants affected by caterpillars, and (III) images of plants damaged by Diabrotica speciosa. This dataset allows training and validation of machine learning models to diagnose, recognize, and classify soybeans affected by caterpillars or Diabrotica speciosa. The images can be processed according to the user’s need since only the size was standardized during the pre-processing phase. Elsevier 2021-12-31 /pmc/articles/PMC8741414/ /pubmed/35028343 http://dx.doi.org/10.1016/j.dib.2021.107756 Text en © 2021 The Author(s) 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 Mignoni, Maria Eloisa Honorato, Aislan Kunst, Rafael Righi, Rodrigo Massuquetti, Angélica Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification |
title | Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification |
title_full | Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification |
title_fullStr | Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification |
title_full_unstemmed | Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification |
title_short | Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification |
title_sort | soybean images dataset for caterpillar and diabrotica speciosa pest detection and classification |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741414/ https://www.ncbi.nlm.nih.gov/pubmed/35028343 http://dx.doi.org/10.1016/j.dib.2021.107756 |
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