Subh775/Rice-Disease-Classification

🌾 Rice Disease Detection Dataset

Overview

The Rice Disease Detection Dataset is a curated collection of high-resolution images showcasing the health conditions of rice crops. It includes images across four distinct classes, enabling the development of machine learning models for detecting and diagnosing rice diseases effectively. This dataset is intended for agricultural researchers, machine learning enthusiasts, and AI practitioners working towards precision farming solutions.

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Labels and Classes

The dataset consists of images categorized into four labels:

1. Brown Spot

   • Caused by Bipolaris oryzae fungus.
   • Symptoms: Oval-shaped brown spots on leaves, leading to reduced photosynthesis and crop yield.

2. Healthy

   • Represents rice crops in optimal health conditions without visible signs of disease.
   • Serves as a baseline for comparison with diseased samples.

3. Leaf Blast

   • A fungal disease caused by Magnaporthe oryzae.
   • Symptoms: Grey or white spindle-shaped lesions with brown margins on the leaves.

4. Neck Blast

   • Another manifestation of Magnaporthe oryzae, attacking the neck of the panicle.
   • Symptoms: Dark lesions around the neck that can lead to panicle breakage or grain loss.

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Dataset Structure

• Images: High-quality images of rice crops under varied conditions and environments.
• Annotations: Each image is labeled with its respective class (Brown Spot, Healthy, Leaf Blast, Neck Blast).
• Total Size: The dataset contains 4078 rows including all classes images and has a total size of 2 GB.

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Applications

This dataset can be used for:

• Training convolutional neural networks (CNNs) for disease classification.
• Developing mobile or web-based applications for farmers to monitor crop(Rice) health.
• Conducting research on automated plant disease detection.

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