Taiwan Tomato Leaves Dataset

Categories Covered:

1. Bacterial Spot: This category includes images of tomato leaves infected by the bacterium Xanthomonas campestris, which causes small, water-soaked lesions that can expand and result in tissue necrosis.
2. Black Leaf Mold: Featuring images of leaves affected by Pseudocercospora fuligena, a fungal disease that produces black spots and mold growth on the underside of leaves.
3. Gray Leaf Spot: This category captures symptoms of Stemphylium solani infection, characterized by grayish or brownish spots that can lead to leaf desiccation.
4. Healthy: This class contains images of undiseased tomato leaves, serving as the baseline for comparison against the diseased categories.
5. Late Blight: A fungal disease caused by Phytophthora infestans, late blight manifests as irregularly shaped lesions with water-soaked margins, often destroying the entire leaf.
6. Powdery Mildew: Powdery mildew, caused by Oidium neolycopersici, appears as white, powdery patches on the leaves, which can eventually result in chlorosis and leaf drop.

Dataset Features:

1. Image Diversity: The dataset is rich in visual variation, encompassing images of both singular and multiple leaves, as well as leaves presented against various backgrounds. This diversity helps to mimic real-world conditions where the appearance of leaves can be affected by environmental factors.
2. Standardized Image Size: To enhance usability in machine learning applications, all images have been resized to a uniform dimension of 227 x 227 pixels, ensuring compatibility with standard deep learning architectures.
3. Practical Use Cases: This dataset is highly suitable for training and evaluating models in the domains of plant disease classification, agricultural disease prediction, and automated plant health monitoring systems.

Potential Applications:

• • Agriculture: Supporting AI models that can identify and predict tomato plant diseases early, improving crop yield and reducing the need for manual inspection.
  • Education: Ideal for use in academic research and projects focused on machine learning, plant pathology, and AI-driven agricultural solutions.
  • Healthcare for Plants: Assisting farmers and agricultural experts in deploying automated disease detection tools to optimize plant health management.