A novel groundnut leaf dataset for detection and classification of groundnut leaf diseases Sasmal, Buddhadev Das, Arunita Gopal Dhal, Krishna Belal Saheb, Sk. Abu Khurma, Ruba Castillo Valdivieso, Pedro Ángel Groundnut Peanut Deep learning 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. 2024-09-05T07:27:51Z 2024-09-05T07:27:51Z 2024-07-25 journal article Sasmal, B. et. al. 55 (2024) 110763. [https://doi.org/10.1016/j.dib.2024.110763] https://hdl.handle.net/10481/93961 10.1016/j.dib.2024.110763 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier