Rice Leaf and Crop Disease Detection Dataset

Data Summary o Total Samples: 10766 2. Raw Data: o Disease Dataset: 2508 samples, including:  Bacterial Leaf Blight: 262  Rice Blast: 592  Tungro: 298  Healthy Leaf: 771  Rice: 585 3. Augmented Data: o Disease Dataset: 8258 samples, including:  Bacterial Leaf Blight: 716  Rice Blast: 2,751  Tungro: 3,447  Healthy Leaf: 601  Rice: 743 Purpose The Rice Leaf and Crop Disease Detection Dataset is a versatile resource for machine learning applications in agriculture.

It is particularly suited for training image-based models, such as Convolutional Neural Networks (CNNs), to: • Detect and classify diseases such as Bacterial Leaf Blight, Rice Blast, and Tungro. • Differentiate between healthy and diseased rice leaves. • Recognize general rice crop features. This dataset facilitates: • Automated Disease Detection: Streamlining monitoring processes in rice cultivation. • Early Intervention Strategies: Enabling timely responses to prevent yield losses. • Enhanced Model Development: Providing a robust dataset for training image-based classifiers.

By fostering precision agriculture practices, this dataset aims to support sustainable farming and the adoption of AI-driven solutions for crop health management.