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Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania
Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experie...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333424/ https://www.ncbi.nlm.nih.gov/pubmed/37441627 http://dx.doi.org/10.1016/j.dib.2023.109322 |
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author | Mduma, Neema Leo, Judith |
author_facet | Mduma, Neema Leo, Judith |
author_sort | Mduma, Neema |
collection | PubMed |
description | Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis, formerly known as Mycosphaerella fijiensis. Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa. |
format | Online Article Text |
id | pubmed-10333424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-103334242023-07-12 Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania Mduma, Neema Leo, Judith Data Brief Data Article Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis, formerly known as Mycosphaerella fijiensis. Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa. Elsevier 2023-06-16 /pmc/articles/PMC10333424/ /pubmed/37441627 http://dx.doi.org/10.1016/j.dib.2023.109322 Text en © 2023 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 Mduma, Neema Leo, Judith Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania |
title | Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania |
title_full | Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania |
title_fullStr | Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania |
title_full_unstemmed | Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania |
title_short | Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania |
title_sort | dataset of banana leaves and stem images for object detection, classification and segmentation: a case of tanzania |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333424/ https://www.ncbi.nlm.nih.gov/pubmed/37441627 http://dx.doi.org/10.1016/j.dib.2023.109322 |
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