Image Dataset for Disease Detection in Black Gram (Vigna mungo) Leaves: A Resource for Machine Learning Research

This dataset presents a curated collection of images of Black Gram (Vigna mungo) leaves, annotated with labels for healthy leaves and various common diseases. Created to support the advancement of machine learning and computer vision models in agricultural disease detection, this dataset is valuable for researchers and practitioners working in botany, plant pathology, agriculture, and artificial intelligence. The dataset is designed to reflect real-world agricultural conditions, providing a robust foundation for developing disease detection and classification models that can aid in crop health monitoring and management.

Dataset Content: The dataset includes a total of 4,038 images representing healthy leaves and five distinct disease categories. Each category offers a range of visual variations, including different background conditions, lighting, and severity of disease symptoms, ensuring comprehensive data diversity. This resource can be used for training, testing, and validating machine learning models for image-based disease classification and detection tasks. The dataset is organized as follows:

Healthy: 545 images
Cercospora leaf spot: 598 images
Leaf Crinkle: 806 images
Insect: 408 images
Yellow Mosaic: 1,681 images

Purpose: The primary aim of this dataset is to facilitate the development of machine learning models that can accurately detect and classify diseases in Black Gram leaves, supporting early diagnosis and promoting effective crop management strategies. This dataset serves as a resource for improving automated plant disease diagnosis, contributing to agricultural sustainability and food security.