Rice Leaf and Crop Disease Detection Dataset

Abstract
This dataset is curated to support the detection and classification of common diseases affecting rice crops, as well as identifying healthy samples. The dataset includes diverse classes and was specifically developed to advance machine learning applications in agricultural disease detection. It provides a valuable resource for researchers seeking to enhance precision farming and crop health monitoring.
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.