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 imagesCercospora leaf spot: 598 imagesLeaf Crinkle: 806 imagesInsect: 408 imagesYellow Mosaic: 1,681 imagesPurpose: 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.

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 imagesCercospora leaf spot: 598 imagesLeaf Crinkle: 806 imagesInsect: 408 imagesYellow Mosaic: 1,681 imagesPurpose: 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.

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 original or raw data total of 4,038 images and augmented data total of 20,190 images (using rotation, brightness adjustment, horizontal flip, and zoom) 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:Original data:Healthy: 545 imagesCercospora leaf spot: 598 imagesLeaf Crinkle: 806 imagesInsect: 408 imagesYellow Mosaic: 1,681 imagesAugmented data:20,190 imagesPurpose: 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.

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 imagesCercospora leaf spot: 598 imagesLeaf Crinkle: 806 imagesInsect: 408 imagesYellow Mosaic: 1,681 imagesPurpose: 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.