Chilli Leaf Disease Image Dataset for Classification and Early Diagnosis in Agriculture

This dataset contains a total of 8,814 high-resolution images (1000×1000) of chilli leaves collected from agricultural fields located in (Ashulia, Narsingdi, Cumilla, Feni, Noakhali, and Laksham) Bangladesh. The dataset is designed to support research in plant disease classification, computer vision, and deep learning-based agricultural AI. The images are categorized into 6 distinct classes: class and description are given below 1. Bacterial (Leaf lesions caused by bacterial infection) 2. Cercospora (Fungal disease with circular spots) 3. Curl Virus (Virus-infected leaves showing curling symptoms) 4. Healthy Leaf (Fresh and disease-free leaves) 5. Nutrient Deficiency (Yellowing and discoloration from poor nutrient supply) 6. Powdery Mildew (White fungal growth on leaf surfaces) No. of Images: 1. Bacterial (1,629) 2. Cercospora (1,898) 3. Curl Virus (1,590) 4. Healthy Leaf (1,647) 5. Nutrient Deficiency (1,207) 6. Powdery Mildew (843) 📌 Total Images: 8,814 📌 Format: JPG and PNG 📌 Resolution: 1000 × 1000 pixels 📌 Color space: RGB 📌 Capture Device: Smartphone 📌 Environment: Real farm conditions — variable lighting, angles & white backgrounds Applications: Image Classification Disease Detection & Monitoring Transfer Learning Deep Learning research in Agriculture Dataset Benchmarking for Vision Models Folder Structure is given below: Chilli_Leaf_Dataset/ ├── Bacterial_Spot/ ├── Cercospora_Leaf_Spot/ ├── Curl_Virus/ ├── Healthy_Leaf/ ├── Nutrient_Deficiency/ └── Powdery_Mildew/