A Comprehensive Dataset for Nirgundi and Eranda Leaf Disease Identification

This paper introduces a comprehensive dataset of leaf images collected fromtwo medicinal plants, Eranda (Ricinus communis) and Nirgundi (Vitex negundo), whichare vital to agricultural and medicinal research. The dataset consists of 5,891 images ofEranda leaves and 5,845 images of Nirgundi leaves, capturing natural variations thatoccur under different environmental conditions. These images include diverseperspectives, lighting conditions, and orientations, ensuring that the collection robustlyrepresents the natural variability in leaf appearance. The dataset was meticulouslycurated to offer a wide spectrum of imaging conditions, making it a valuable resource forresearchers investigating plant morphology, environmental impacts on plant health, andthe early detection of potential plant diseases. In addition to its sheer size, the datasetincludes images representing various stages of leaf health, providing a rich resource forexploratory data analysis and the development of diagnostic tools. While the datasetholds promise for a range of applications\u2014including machine learning model trainingand validation\u2014the focus of this paper is to document and share this extensive imagecollection. By making this dataset publicly available, we aim to support the scientificcommunity in developing innovative solutions for plant health monitoring and contributeto the broader field of agricultural research.