GTLD: A dataset of tomato leaf diseases
Published: 11 July 2024| Version 1 | DOI: 10.17632/2bdfjb99k5.1
Contributor:
DEBABRAT BHARALIDescription
The images in the dataset were taken by hand using a DSLR camera and a high-quality cell phone in direct sunshine, with some even taking place beneath the plant's shaded sections. Images of common plant diseases such leaf miner, leaf curl, blight, cutwork infected leaf, leaf spot, and Alternaria were gathered from various tomato cultivation fields. An expert assisted in sorting the randomly selected images. The images were downsized from their original 3096 × 4128 dimensions to 256 × 256. Researchers and students from various backgrounds can train, test, and validate classification algorithms using our suggested dataset.
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Institutions
University of Science & Technology Meghalaya
Categories
Machine Learning, Deep Learning