Tomato leaf dataset: A dataset for multiclass disease detection and classification

Area of Science:

• Agricultural Science
• Computer Vision
• Machine Learning

Background:

• Tomato cultivation is vital to Bangladesh's economy, but diseases threaten crop yields.
• Early detection of tomato plant diseases is crucial for effective management and farmer livelihoods.

Purpose of the Study:

• To create and share a comprehensive dataset of tomato leaf images for automated disease diagnosis.
• To facilitate the development of machine learning models for early and accurate disease detection in tomato plants.

Main Methods:

• Collected 731 high-resolution images of tomato leaves exhibiting six common diseases (Early Blight, Black Spot, Late Blight, Leaf Mold, Bacterial Spot, Target Spot) and healthy samples.
• Structured the dataset for use in computer vision and machine learning applications.

Main Results:

• A publicly available, categorized dataset of tomato leaf images for disease identification.
• The dataset supports research in AI-driven agricultural tools for disease management.

Conclusions:

• The dataset accelerates research in precision agriculture and AI-powered crop protection.
• Empowers the development of tools to enhance tomato disease management, improving yields and sustainability.