Tomato leaf diseases

This dataset consists of over 2600 images of tomato leaves collected from Khagan, Charabag, located near Daffodil International University. The images include various diseases that affect tomato plants, including viral, bacterial, and fungal infections. The dataset was classified by professionals who labeled the images according to the type of disease or healthy condition observed in the leaves.

After classification, the images were compressed and their size was reduced by 80% to facilitate easier handling and analysis. This dataset is intended for use in research, diagnostics, and machine learning applications focused on plant disease recognition.

Selection of Disease Types: A total of 10 different diseases affecting tomato plants were identified and categorized. These diseases include both viral, bacterial, fungal, and insect-related diseases.
Image Acquisition:
Over 2600 images were captured from tomato plants with visible symptoms of these diseases.
The leaf samples were sourced from a variety of regions to ensure diversity in the dataset.
The images were taken in natural settings, ensuring the images represent realistic field conditions.
Camera Setup: All images were taken using the phone’s primary camera (iPhone 11, 12MP) to ensure consistent image quality across all samples.
Focus was maintained on the specific regions where symptoms of diseases appeared, and proper lighting conditions were ensured.
Disease Categories:

Tomato Leaf Curl Virus: 394 images
Spider Mites: 307 images
Leaf Mold: 66 images
Leaf Miner: 519 images
Late Blight: 166 images
Insect Damage: 336 images
Healthy Leaves: 103 images
Early Blight: 204 images
Cercospora Leaf Mold: 156 images
Bacterial Spot: 376 images
Class 16 (Uncategorized/Other): 32 images
Data Labeling:

The images were manually labeled by experts or through observational methods to categorize them under specific diseases or healthy leaves.
Labels were applied based on clear visible symptoms such as color changes, mold growth, leaf curling, or spots that are characteristic of specific diseases.
Compression and Storage:

The dataset was compressed by 80% to make it manageable for use and to facilitate faster loading during processing.
Images were stored in a common image format JPG for ease of access.