Guava Leaf Disease Analysis Dataset (GLDAD)
Description
The Guava Leaf Disease Analysis Dataset (GLDAD) is a collection of images designed for the analysis of leaf diseases in guava plants. This dataset includes images of guava leaves that have been categorized into different disease types, as well as healthy leaves. Each image is represented in RGB format and varies in size and quality. This dataset is primarily aimed at research on plant disease detection and classification using image processing and machine learning techniques. Dataset Details: Folders: The dataset is organized into 6 main folders, each representing a different condition of the guava leaves: Anthracnose: Leaves affected by anthracnose disease. Healthy: Healthy, unaffected leaves. Insect Bite: Leaves showing damage from insect bites. Multiple: Leaves with multiple types of diseases or issues. Scorch: Leaves exhibiting scorch marks from environmental stress or disease. YLD (Yellowing): Leaves showing yellowing symptoms due to various factors, like nutrient deficiency or disease. Total Number of Images: Each category contains approximately 11,000 images, making the total number of images in the dataset around 66,000 images. Image Specifications: Dimensions: The majority of the images are of size 256x256 pixels, though some images have been transformed to other sizes (e.g., 200x200 pixels). File Format: JPEG format, stored in RGB color mode. Image Transformations: Each image has undergone multiple transformations, such as rotation, cropping, scaling, flipping, adding noise, etc., to augment the dataset and improve the robustness of machine learning models. File Size: Varies from around 3 KB to 10 KB per image, depending on transformations applied. Additional Metadata: Images were captured on January 11, 2025. The dataset includes both original images and their transformed counterparts (e.g., transformed_1.jpg, transformed_2.jpg, etc.). Each image’s metadata includes the creation and modification timestamps, though DPI and compression are not available. Creation Time: Sat Jan 11 13:45:28 2025 More Raw data : Shuvo Kumar Basak. (2025). Guava Leaf Disease Dataset (GLDD) [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/6462671 Note for Researchers Using the Guava Leaf Disease Analysis Dataset (GLDAD): This dataset, titled Guava Leaf Disease Analysis Dataset (GLDAD), was created by Shuvo Kumar Basak. If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.
Files
Steps to reproduce
Resize Images: Normalize the images to a uniform size (e.g., 256x256 pixels) for input into machine learning models. Convert Images: If necessary, convert images to grayscale or other color models depending on the requirements of your model. Data Augmentation: Apply transformations like rotation, flipping, noise addition, and random cropping to augment the dataset and improve model robustness.