Watermelon Disease Recognition Dataset

Published: 29 September 2023| Version 1 | DOI: 10.17632/ntzym554jp.1
Mohammad Imtiaz Nakib,


(1) Crop diseases significantly impact agricultural productivity and quality, particularly in watermelon cultivation. Traditional disease diagnosis methods are time-consuming and subjective, creating a demand for efficient machine vision-based disease detection models. (2) We present a comprehensive watermelon dataset, including images of healthy watermelons and those afflicted by Mosaic Virus, Anthracnose, and Downy Mildew Disease. Collected in collaboration with experts on June 25, 2023, from the Regional Horticulture Research Station in Lebukhali, Patuakhali, Bangladesh. This dataset aids in early disease detection. (3) Watermelons are essential for global food security but face challenges like disease threats and limited farmer training. (4) Our dataset supports the development of advanced machine vision algorithms for early disease identification. It contains four watermelon classes: Mosaic Virus, Healthy, Anthracnose, and Downy Mildew, with 1155 original images and 5775 augmented images. (5) The dataset is accessible on the Mendeley repository, facilitating research and empowering farmers to protect watermelon production and agricultural stability.



American International University Bangladesh


Computer Vision, Image Processing, Image Classification, Recognition, Plant Diseases, Deep Learning