Comprehensive Dataset of Luffa aegyptiaca Diseases in Dhaka District: A Valuable Resource for Agricultural Research

Published: 22 December 2023| Version 3 | DOI: 10.17632/dkptcnjn42.3
Md Tariqul Islam


Greetings Researchers and Plant Pathologists, Delve into the intricate world of "Luffa aegyptiaca 480," an extensive dataset meticulously compiled from the agrarian landscapes of Jalkuri, Narayanganj, and Khagan, Ashuliya, situated within the Dhaka district of Bangladesh. This dataset stands as a significant contribution to the field, offering an unparalleled examination of Luffa aegyptiaca plants afflicted with various diseases. Organized into three distinct subfolders—Cucumber Mosaic Virus, Downey Mildew, and Leaf Spot—this dataset comprises 6533 high-resolution JPEG images, each meticulously captured at a resolution of 854x480 pixels. The Cucumber Mosaic Virus subfolder encompasses 2004 images, providing a detailed visual representation of the viral pathology affecting Luffa aegyptiaca. In the Downey Mildew subfolder, 2024 images elucidate the nuanced manifestations of fungal infection, while the Leaf Spot subfolder, boasting 2505 images, offers a comprehensive exploration of leaf spot diseases. Collected from the regions of Jalkuri and Khagan, this dataset not only provides a visual narrative but also serves as a unique repository, currently unparalleled on the internet. Researchers, agronomists, and scholars in plant pathology will find this dataset to be an invaluable asset for in-depth analysis and understanding of diseases affecting Luffa aegyptiaca crops. Embark on a scholarly exploration, unravel the complexities of plant-pathogen interactions, and augment your research endeavors with the rich visual data encapsulated in "Luffa aegyptiaca 480." This dataset beckons as a cornerstone for agricultural research, offering a nuanced perspective into the challenges faced by Luffa aegyptiaca in the Dhaka district. Happy Exploring!



Image Processing, Data Science, Machine Learning, Data Collection in Agriculture, Leaf Studies, Convolutional Neural Network, Deep Learning, Transformer-Based Deep Learning