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Dataset . 2023
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
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Common Beans Imagery Dataset for Early Detection of Crop Diseases

Authors: Laizer, Hudson; Mduma, Neema; Machuve, Dina; Lyimo, Tumaini; Babirye, Claire; Swai, Jenifa; Siwingwa, Adam;

Common Beans Imagery Dataset for Early Detection of Crop Diseases

Abstract

The annotated dataset consists of common beans leaf imagery for early diseases detection. The common beans crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 20th October 2022 and 10th April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Bean anthracnose, and (ii) Bean rust diseases in common beans. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones. All images are in the .zip files; “anthra.zip” has 13,531 images, “healthy.zip” has 24,973 images, and “rust.zip” has 20,568 images. A total of 59,072 image files are labelled. This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.

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Keywords

Bean rust, Leaf imagery, Crop diseases, Common bean, Bean anthracnose

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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