Irish Potato Imagery Dataset for Detection of Early and Late Blight Diseases
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Date
2025-04-03
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Publisher
Elsevier
Abstract
This dataset comprises of 58,709 annotated images of irish
potato leaves, categorized into three classes (healthy, early
blight and late blight). The data was collected over six
months from smallholder farms in Southern Highlands Tan-
zania, using Samsung Galaxy A03 smartphones with 8-
megapixel camera. Researchers, farmers and agricultural ex-
tension officers were trained to capture images under di-
verse conditions, including varying lighting, angles and back-
grounds to ensure the dataset is diverse and representative.
Plant pathologists were used to validate the images to en-
sure and enhance the reliability of the labels. Pre-processing
steps such as duplicate removal, filtering of irrelevant im-
ages, annotation and metadata integration were applied re-
sulting in a high-quality dataset. The dataset is organized
into three folders (healthy, early blight and late blight) and
is freely available on the Zenodo repository to promote ac-
cessibility for researchers working in the field of plant dis-
eases. This dataset holds significant potential for reuse in
training machine learning models for crop disease detec-
tion, transfer learning and data augmentation studies. By en-
abling early detection and classification of potato diseases,
the dataset supports the development of innovative agricul-
tural tools aimed at reducing crop losses and enhancing food
security in Sub-Saharan Africa. Its robust design and regional
specificity make it a valuable resource for advancing research
and innovation in sustainable farming practices.
Sustainable Development Goals
This research article was published by Elsevier on 3 April 2025
Keywords
Computer Science