Multi-Crop Disease Dataset

This dataset presents a comprehensive collection of annotated images of diseased and healthy leaves across five important agricultural crops: Banana, Chilli, Radish, Groundnut, and Cauliflower. The dataset was created to support research in plant disease detection, precision agriculture, and deep learning-based crop monitoring systems.

Research Hypothesis
Early detection and classification of crop diseases using image-based AI models can significantly reduce yield loss and improve sustainable farming practices. This dataset enables training and evaluation of such AI models across multiple crops and diverse disease types.

What the Data Shows
The dataset contains over 23,000 images captured in real agricultural settings, labeled using bounding box annotations. Each crop includes both healthy and multiple disease-specific categories, with more than 30 total classes (e.g., Sigatoka, Leaf Curl, Anthracnose, Rust, Downy Mildew, Black Rot, etc.).

Notable Features
High-quality images (640×640 resolution), collected using digital cameras and 200MP mobile phone cameras

Annotated with bounding boxes for object detection tasks

Data collected from Chengalpattu, Kanchipuram, and Krishnagiri districts, Tamil Nadu, India

Covers real-world variations in lighting, leaf orientation, and disease stages

How to Interpret and Use the Data
Images are organized by crop name and disease class

Annotations are provided in YOLO format (can be converted to COCO/VOC)

Suitable for training CNN, YOLO, Faster R-CNN, or ViT models for plant disease classification and localization

Ideal for researchers working on edge AI, TinyML, and mobile agriculture apps

Potential Applications
Real-time disease diagnosis in smart farming systems

Academic research in plant pathology and computer vision

Benchmarking object detection models in agricultural settings