TANG16 / ToMV-KELM-Classifier

A KELM(Kernel Extreme Learning Machine) based Tomato Mosaic Virus Leaf Classifier

ToMV Leaf Classification

This project implements a machine learning model using image texture features to classify Tomato Mosaic Virus (ToMV) disease in tomato leaves.

Project Highlights:

• Accuracy: Achieves over 99% accuracy on the test set.
• Model: Kernel Extreme Learning Machine (KELM) with RBF kernel.
• Features: Gray level co-occurrence matrix (GLCM) features and color histograms.

Screeshots

Requirements:

• Python 3.6+
• Streamlit
• OpenCV
• NumPy
• scikit-image
• Pickle

Getting Started:

1. Clone: git clone https://github.com/your-username/ToMV-KELM-Classifier.git
2. Install: pip install -r requirements.txt
3. Download: Place pre-trained models (finalized_train_data.pkl and finalized_model.pkl) in the Model_Files directory.
4. Run: streamlit run main.py

Using the Application:

1. Upload a tomato leaf image.
2. Click "Get Classification".
3. The model predicts the leaf health status (Healthy or Diseased).

Model Description:

The KELM model utilizes an RBF kernel. It extracts texture features from images and predicts health status based on those features.

Disclaimer:

Prototype model not intended for commercial use.