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

Data Brief

Mbeya University of Science and Technology, Department of Natural Sciences , P o Box 131, Mbeya - Tanzania.

Published: June 2023

AI Article Synopsis

  • Maize is a key food and cash crop for smallholder farmers in Africa, but it's severely impacted by diseases like Maize Lethal Necrosis and Maize Streak, threatening food security and income.
  • The paper presents a large dataset of 18,148 well-curated images of healthy and diseased maize leaves, captured with smartphone cameras in Tanzania, aimed at supporting machine learning and computer vision projects.
  • This dataset intends to help develop tools that aid farmers in diagnosing maize diseases and improving crop yields, ultimately addressing food security challenges in Tanzania and beyond.

Article Abstract

Maize 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121386PMC
http://dx.doi.org/10.1016/j.dib.2023.109108DOI Listing

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