A novel groundnut leaf dataset for detection and classification of groundnut leaf diseases

Published: 13 February 2024| Version 2 | DOI: 10.17632/x6x5jkk873.2
Buddhadev Sasmal, Arunita Das, Krishna Gopal Dhal, Belal Saheb, Ruba Abu Khurma, Pedro A. Castillo-Valdivieso


The image of the groundnut leaves was collected from the agricultural land of Ramchandrapur village in Purba Medinipur district of West Bengal, India, Pin: 721429, which is both healthy and infected with Leaf spot (early and late), Alternaria leaf spot, Rust, and Rosette. The dataset contains 1,720 jpg images in 4624 × 3472 pixel format, each labelled with the image number and the name of the image. Data were uploaded to the repository in 5 distinct folders: 1 folder for healthy data, 1 folder for Leaf spot (early and late), 1 folder for Alternaria leaf spot, 1 folder for Rust, and 1 folder for Rosette diseases. In addition, each folder's name reflected the associated image class. The HEALTHY folder comprises a collection of images depicting groundnut leaves in a healthy condition. The LEAF SPOT (EARLY AND LATE) folder contains images of groundnut leaves affected by Leaf spot (early and late). The folder titled ALTERNARIA LEAF SPOT contains images depicting groundnut leaves that have been infected by Alternaria leaf spot. The RUST folder comprises images of groundnut leaves infected by Rust disease. The ROSETTE folder contains images of groundnut leaves infected by Rosette disease. The images were divided into distinct directories to facilitate the process of sharing and downloading data.



Middle East University, Universidad de Granada, Midnapore College


Computer Science, Machine Learning, Precision Agriculture, Deep Learning