Irish potato imagery dataset for detection of early and late blight diseasesZenodo
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 Tanzania, using Samsung Galaxy A03 smartphones with 8-megapixel camera...
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Elsevier
2025-06-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925002811 |
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| author | Hudson Laizer Neema Mduma |
| author_facet | Hudson Laizer Neema Mduma |
| author_sort | Hudson Laizer |
| collection | DOAJ |
| description | 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 Tanzania, using Samsung Galaxy A03 smartphones with 8-megapixel camera. Researchers, farmers and agricultural extension officers were trained to capture images under diverse conditions, including varying lighting, angles and backgrounds to ensure the dataset is diverse and representative. Plant pathologists were used to validate the images to ensure and enhance the reliability of the labels. Pre-processing steps such as duplicate removal, filtering of irrelevant images, annotation and metadata integration were applied resulting 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 accessibility for researchers working in the field of plant diseases. This dataset holds significant potential for reuse in training machine learning models for crop disease detection, transfer learning and data augmentation studies. By enabling early detection and classification of potato diseases, the dataset supports the development of innovative agricultural 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. |
| format | Article |
| id | doaj-art-6ec2f17281714288b3c732ba615c8d6d |
| institution | OA Journals |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-6ec2f17281714288b3c732ba615c8d6d2025-08-20T02:31:00ZengElsevierData in Brief2352-34092025-06-016011154910.1016/j.dib.2025.111549Irish potato imagery dataset for detection of early and late blight diseasesZenodoHudson Laizer0Neema Mduma1Corresponding author.; The Nelson Mandela African Institution of Science and Technology, P O Box 447, Tengeru, Arusha, TanzaniaThe Nelson Mandela African Institution of Science and Technology, P O Box 447, Tengeru, Arusha, TanzaniaThis 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 Tanzania, using Samsung Galaxy A03 smartphones with 8-megapixel camera. Researchers, farmers and agricultural extension officers were trained to capture images under diverse conditions, including varying lighting, angles and backgrounds to ensure the dataset is diverse and representative. Plant pathologists were used to validate the images to ensure and enhance the reliability of the labels. Pre-processing steps such as duplicate removal, filtering of irrelevant images, annotation and metadata integration were applied resulting 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 accessibility for researchers working in the field of plant diseases. This dataset holds significant potential for reuse in training machine learning models for crop disease detection, transfer learning and data augmentation studies. By enabling early detection and classification of potato diseases, the dataset supports the development of innovative agricultural 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.http://www.sciencedirect.com/science/article/pii/S2352340925002811Solanum tuberosumDisease detectionMachine learningComputer visionSmallholder farming systemImage annotation |
| spellingShingle | Hudson Laizer Neema Mduma Irish potato imagery dataset for detection of early and late blight diseasesZenodo Data in Brief Solanum tuberosum Disease detection Machine learning Computer vision Smallholder farming system Image annotation |
| title | Irish potato imagery dataset for detection of early and late blight diseasesZenodo |
| title_full | Irish potato imagery dataset for detection of early and late blight diseasesZenodo |
| title_fullStr | Irish potato imagery dataset for detection of early and late blight diseasesZenodo |
| title_full_unstemmed | Irish potato imagery dataset for detection of early and late blight diseasesZenodo |
| title_short | Irish potato imagery dataset for detection of early and late blight diseasesZenodo |
| title_sort | irish potato imagery dataset for detection of early and late blight diseaseszenodo |
| topic | Solanum tuberosum Disease detection Machine learning Computer vision Smallholder farming system Image annotation |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925002811 |
| work_keys_str_mv | AT hudsonlaizer irishpotatoimagerydatasetfordetectionofearlyandlateblightdiseaseszenodo AT neemamduma irishpotatoimagerydatasetfordetectionofearlyandlateblightdiseaseszenodo |