Medicinal Plant Leaf Disease Dataset

This dataset contains 2,547 high-resolution leaf images (4000 × 3000 px, 4:3 aspect ratio) of three medicinal plant species, Kalanchoe pinnata (PatharKuchi), Azadirachta indica (Neem), and Ocimum tenuiflorum (Tulsi), collected from Senbag Upazila, Noakhali, Bangladesh, during August 2025. Each species includes one healthy and three disease or symptom classes, forming twelve balanced categories verified by agricultural experts.

All images were captured in natural daylight using a 50 MP Honor 200 smartphone camera, ensuring clear color and texture representation. Images were taken from multiple angles and distances to preserve natural variations in illumination and background. Leaf samples were collected directly from local fields and home-grown medicinal gardens.

Class distribution:

Kalanchoe pinnata (PatharKuchi): Healthy (209), Web Blight (213), Yellow (204), Yellow Blight (213) → Total 839

Azadirachta indica (Neem): Healthy (244), Leaf Spot (211), Web Blight (205), Yellow (225) → Total 885

Ocimum tenuiflorum (Tulsi): Healthy (213), Downy Mildew (200), Web Blight (205), Yellow Spot (205) → Total 823
Grand Total: 2,547 images across 12 classes.

Each image is stored in .jpg format (4000 × 3000 px) and named following a class-based convention (e.g., Neem_Healthy, Neem_LeafSpot, Tulsi_Healthy, Patharkuchi_YellowBlight). All files are placed in a single folder, with class names embedded in filenames for convenient parsing and automatic label extraction during model training.

The dataset is designed for AI-based plant disease detection, particularly hybrid CNN/Transformer models and Explainable AI (XAI) research. It enables studies in agricultural image classification, medicinal plant health monitoring, and digital pathology applications.

Verification and labeling were conducted under the supervision of the Upazila Agriculture Officer, Senbag, on 19 October 2025, ensuring correct disease identification and class validity.