Plant Leaf Disease Detection Project

Overview

This project utilizes Convolutional Neural Networks (CNN) for plant leaf disease detection, employing image processing techniques, feature extraction, and selection. The classification algorithm employed enhances the accuracy of disease identification.

Features

1. CNN Architecture: Implemented a robust CNN architecture for effective feature extraction from plant leaf images.

2. Image Processing: Utilized advanced image processing techniques to enhance the quality and clarity of input images, aiding in accurate disease identification.

3. Feature Extraction and Selection: Extracted relevant features from plant leaf images and employed feature selection techniques to optimize the model's performance.

4. Classification Algorithm: Integrated a powerful classification algorithm to accurately identify and classify plant diseases based on the extracted features.

Requirements

• Python 3.x
• TensorFlow
• Keras
• OpenCV
• NumPy
• Scikit-learn

Usage

1. Install the required dependencies using pip install -r requirements.txt.
2. Train the model using the provided dataset by running train_model.py.
3. Test the trained model on new images using predict.py.

Dataset

The project uses a curated dataset of plant leaf images with labeled disease categories. The dataset is available at [link_to_dataset].

Results

Our model achieved 93 % accuracy on the test set, showcasing its effectiveness in plant disease detection.