Crop Disease Classification

An AI-powered web application that detects crop diseases using deep learning

Features

• Crop Disease Detection: Utilizes a CNN model to classify diseases in potato crops with 88% accuracy.
• User-friendly Interface: Built with Flask for easy image upload and real-time disease classification.
• Deep Learning Model: Trained a CNN using TensorFlow and Keras for high-accuracy classification.

Tech Stack

• Deep Learning: TensorFlow, Keras
• Backend: Flask
• Data Processing: OpenCV, NumPy, Pandas

Installation & Setup

Prerequisites

• Python 3.x
• pip

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/crop-disease-classification.git
   cd crop-disease-classification

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask web application:

   python app.py

Usage

1. Open the web application in your browser.
2. Upload an image of the affected crop.
3. The system predicts the disease.

Dataset

The model was trained on:

• Potato crop disease dataset with labeled images.
• Data preprocessing and augmentation were performed using OpenCV and NumPy.

Model Details

• Algorithm: Convolutional Neural Network (CNN)
• Training Library: TensorFlow, Keras
• Feature Engineering: Image preprocessing and augmentation

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.