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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...

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Detalles Bibliográficos
Autores principales: Mduma, Neema, Leo, Judith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
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.
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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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