Machine Learning Imagery Dataset for Maize Crop: A Case of Tanzania

dc.contributor.authorMduma, Neema
dc.contributor.authorLaizer, Hudson
dc.date.accessioned2023-04-11T09:11:40Z
dc.date.available2023-04-11T09:11:40Z
dc.date.issued2023-03-31
dc.descriptionThis research article was published by Elsevier in 2023en_US
dc.description.abstractMaize is one of the most important staple food and cash crops that are largely produced by majority of smallholder farmers throughout the humid and sub-humid tropic of Africa. Despite its significance in the household food security and income, diseases, especially Maize Lethal Necrosis and Maize Streak, have been significantly affecting production of this crop. This paper offers a dataset of well curated images of maize crop for both healthy and diseased leaves captured using smartphone camera in Tanzania. The dataset is the largest publicly accessible dataset for maize leaves with a total of 18,148 images, which can be used to develop machine learning models for the early detection of diseases affecting maize. Moreover, the dataset can be used to support computer vision applications such as image segmentation, object detection and classification. The goal of generating this dataset is to assist the development of comprehensive tools that will help farmers in the diagnosis of diseases and the enhancement of maize yields thus eradicating the problem of fod security in Tanzania and other parts in Africa.en_US
dc.identifier.urihttps://doi.org/10.1016/j.dib.2023.109108
dc.identifier.urihttps://dspace.nm-aist.ac.tz/handle/20.500.12479/1860
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectMaizeen_US
dc.subjectMaize Lethal Necrosisen_US
dc.subjectMaize Streak Virusen_US
dc.titleMachine Learning Imagery Dataset for Maize Crop: A Case of Tanzaniaen_US
dc.typeArticleen_US

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