🌱 Plant Disease Detection Project

📋 Project Overview

This project leverages advanced image processing, machine learning, and deep learning to detect plant diseases from leaf images. A complete pipeline has been developed, combining:

Preprocessing:

🖼️ Image Augmentation

❌ Noise Removal

🌟 Contrast Enhancement

Texture Analysis:

Feature extraction using:

📊 GLCM (Gray Level Co-occurrence Matrix)

🖼️ LBP (Local Binary Patterns)

🌈 RGB Histogram

🎨 HSV Histogram

Machine Learning Models:

Hyperparameter tuning with Optuna on: 🌟 XGBoost 🌳 Random Forest 📈 KNN 📉 SVM 🌲 Decision Tree Best Results: XGBoost with HSV features: ✅ Validation Accuracy: 96% ✅ Test Accuracy: 84% Deep Learning with MobileNet:

Achieved: ✅ Test Accuracy: 95%

Deployment:

🔧 Backend: Flask 🌐 Frontend: React

📂 Dataset You can download the dataset from the following link:

🔗 Plant Disease Dataset

🚀 How to Operate the Project

🛠️ Step 1: Start the Backend (Flask)

Install required libraries:

Run the Flask app: python app.py

🌐 Step 2: Start the Frontend (React)

• Navigate to the frontend directory: cd frontend

• Install required packages: npm install

• Run the React app:

npm start

🖼️ Step 3: Test with Images

Open your browser and navigate to http://localhost:3000. Upload an image of a plant leaf. View the predictions and analysis in real-time!

📊 Key Results Machine Learning (XGBoost + HSV Features):

✅ Validation Accuracy: 96% ✅ Test Accuracy: 84%

MobileNet:

✅ Test Accuracy: 95%

Deployment:

🌟 Fully functional web app with Flask backend and React frontend.