@misc{10481/93961,
year = {2024},
month = {7},
url = {https://hdl.handle.net/10481/93961},
abstract = {Groundnut (Arachis hypogaea) is a widely cultivated legume crop that plays a vital role in global agriculture and food se- curity. It is a major source of vegetable oil and protein for human consumption, as well as a cash crop for farmers in many regions. Despite the importance of this crop to house- hold food security and income, diseases, particularly Leaf spot (early and late), Alternaria leaf spot, Rust, and Rosette, have had a significant impact on its production. Deep learn- ing (DL) techniques, especially convolutional neural networks (CNNs), have demonstrated significant ability for early di- agnosis of the plant leaf diseases. However, the availabil- ity of groundnut-specific datasets for training and evalua- tion of DL models is limited, hindering the development and benchmarking of groundnut-related deep learning applica- tions. Therefore, this study provides a dataset of groundnut leaf images, both diseased and healthy, captured in real culti- vation fields at Ramchandrapur, Purba Medinipur, West Ben- gal, using a smartphone camera. The dataset contains a to- tal of 1720 original images, that can be utilized to train DL models to detect groundnut leaf diseases at an early stage.},
organization = {Ministerio Español de Ciencia e Innovación under projects PID2020-115570GB-C22 MCIN/AEI/10.13039/50110 0 011033 and PID2023-147409NB-C21 MICIU/AEI/10.13039/50110 0 011033 , the C-ING-027-UGR23},
organization = {Cátedra de Empresa Tecnología para las Personas (UGR-Fujitsu)},
publisher = {Elsevier},
keywords = {Groundnut},
keywords = {Peanut},
keywords = {Deep learning},
title = {A novel groundnut leaf dataset for detection and classification of groundnut leaf diseases},
doi = {10.1016/j.dib.2024.110763},
author = {Sasmal, Buddhadev and Das, Arunita and Gopal Dhal, Krishna and Belal Saheb, Sk. and Abu Khurma, Ruba and Castillo Valdivieso, Pedro Ángel},
}