Real-World Dataset for Classifying Diseases in Papaya Leaves

The Real-World Dataset for Classifying Diseases in Papaya Leaves offers a collection of 419 high-quality JPEG images, showcasing a variety of both healthy and diseased papaya leaves captured in natural outdoor settings. Created with real agricultural environments in mind, this dataset is ideal for training machine learning models, especially those focused on plant disease detection and image classification. It features six clearly labeled categories—Bacterial Blight (15 images), Carica Insect Hole (54), Curled Yellow Spot (86), Healthy Leaf (39), Pathogen Symptoms (195), and Yellow Necrotic Spots Holes (30)—each reflecting unique symptoms like leaf curling, spots, holes, or discoloration. The photos include natural variations in lighting, angles, and backgrounds, helping models better understand what these diseases look like in real farming conditions. Whether you're building an AI-based diagnostic tool or researching plant health, this dataset provides a rich and practical foundation.