🌿 Plant Disease AI Assistant

Advanced AI-powered plant health diagnosis using traditional machine learning

🚀 Live Demo • 📖 Documentation • 🛠️ Installation • 🤝 Contributing

📋 Table of Contents

🌟 Overview
✨ Features
🎯 Supported Diseases
🧠 AI Models
🚀 Live Demo
📦 Installation
🛠️ Usage
🏗️ Architecture
📊 Performance
🔧 Configuration
🤝 Contributing
📄 License
👨‍💻 Author

🌟 Overview

Plant Disease AI Assistant is a comprehensive web application that leverages traditional machine learning algorithms to diagnose plant leaf diseases with high accuracy. Built with Streamlit and powered by multiple ML models, it provides real-time analysis, treatment recommendations, and detailed analytics for plant health management.

🎯 Key Highlights

🔬 Multi-Model AI: 6 different machine learning algorithms for robust predictions
🎨 Modern UI: Beautiful, responsive interface with multiple themes
🔐 Secure Authentication: Role-based access control system
📊 Advanced Analytics: Comprehensive tracking and visualization
⚡ Real-time Processing: Instant disease detection and analysis
📱 User-Friendly: Intuitive interface for both experts and beginners

✨ Features

🔬 AI-Powered Analysis

🖼️ Image Upload: Support for JPG, PNG, JPEG formats
🔍 Feature Extraction: Advanced color and texture analysis (LBP)
🎯 Multi-Model Prediction: 6 different ML algorithms
📈 Confidence Scoring: Detailed probability analysis
🔄 Batch Processing: Process multiple images simultaneously

🎨 User Experience

🌞 Multiple Themes: Light, Dark, and Colorful modes
📱 Responsive Design: Works on desktop and mobile devices
🔔 Smart Notifications: Real-time alerts and updates
📊 Interactive Dashboards: Beautiful charts and visualizations
💾 Data Export: CSV and JSON export functionality

🔐 Security & Management

👤 User Authentication: Secure login/registration system
👥 Role Management: Admin and user roles with different privileges
📝 User Management: Admin panel for user administration
🔒 Session Management: Secure session handling
📈 Usage Analytics: Track user activity and system performance

📊 Analytics & Reporting

📈 Prediction History: Complete tracking of all analyses
📊 Performance Metrics: Model accuracy and confidence trends
🎯 Health Distribution: Visual representation of plant health
📋 Treatment Tracking: Monitor treatment effectiveness
📥 Data Export: Download reports in multiple formats

🎯 Supported Diseases

The application can classify plant leaves into 4 distinct categories:

Disease Type	Severity	Description	Treatment
🟢 Healthy	None	No disease detected	Continue regular care
🟠 Rust	Medium	Fungal disease with orange spots	Apply copper-based fungicide
🟤 Scab	Medium	Bacterial disease with dark lesions	Use sulfur-based fungicide
🔴 Multiple Diseases	High	Multiple disease symptoms	Consult plant specialist

🧠 AI Models

The application uses 6 different machine learning models for robust predictions:

Model	Type	Accuracy	Speed	Use Case
🌳 Random Forest	Ensemble	94.2%	Fast	General accuracy
🎯 SVM (RBF)	Kernel Method	91.8%	Medium	Complex patterns
🚀 Gradient Boosting	Boosting	93.5%	Slower	High accuracy
🗳️ Voting Ensemble	Combined	96.1%	Medium	Best overall
👥 K-Nearest Neighbors	Lazy Learner	89.3%	Fast	Simple patterns
📈 Logistic Regression	Linear	87.6%	Very Fast	Baseline comparison

🔬 Feature Extraction

The system extracts 16 features from each image:

Color Features: RGB mean and standard deviation (6 features)
Texture Features: Local Binary Pattern (LBP) histogram (10 features)

🚀 Live Demo

Experience the application live:

🌿 Try Plant Disease AI Assistant

Demo Credentials:

👨‍💼 Admin: admin / admin123
👤 User: user / user123

📦 Installation

Prerequisites

Python 3.8 or higher
pip package manager
Git

Step 1: Clone the Repository

git clone https://github.com/Its-Kratik/Leafwise_Assistant.git
cd Leafwise_Assistant

Step 2: Create Virtual Environment

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Run the Application

streamlit run app.py

The application will be available at: http://localhost:8501

🛠️ Usage

🔐 Getting Started

Register/Login: Create an account or use demo credentials
Choose Theme: Select your preferred interface theme
Upload Image: Drag and drop or select a plant leaf image
Analyze: Click "Analyze Now" for instant results
Review Results: View prediction, confidence, and treatment recommendations

🔬 AI Scanner Features

Quick Detection

Single image analysis
Real-time processing
Instant results with confidence scores
Treatment recommendations

Advanced Classification

Multiple model comparison
Detailed probability analysis
Feature extraction visualization
Batch processing capability

Treatment Guide

Disease-specific recommendations
Treatment schedules
Prevention tips
Progress tracking

📊 Analytics Dashboard

Prediction History: Complete log of all analyses
Performance Metrics: Model accuracy and trends
Health Distribution: Visual representation of results
Export Options: Download data in CSV/JSON format

🏗️ Architecture

Plant Disease AI Assistant/
├── 📁 app.py                    # Main Streamlit application
├── 📁 requirements.txt          # Python dependencies
├── 📁 README.md                 # Project documentation
├── 📁 LICENSE                   # MIT License
├── 📁 .devcontainer/           # Development container config
│   └── 📄 devcontainer.json
└── 📁 models/                  # Trained ML models
    ├── 🌳 plant_disease_rf_model.joblib
    ├── 🎯 plant_disease_svm_model.joblib
    ├── 🚀 plant_disease_gb_model.joblib
    ├── 🗳️ plant_disease_voting_model.joblib
    ├── 👥 plant_disease_knn_model.joblib
    └── 📈 plant_disease_logreg_model.joblib

🔧 Technology Stack

Component	Technology	Purpose
Frontend	Streamlit	Web interface and user experience
ML Framework	Scikit-learn	Machine learning algorithms
Image Processing	OpenCV, PIL	Image analysis and feature extraction
Data Processing	Pandas, NumPy	Data manipulation and analysis
Visualization	Plotly, Matplotlib	Charts and graphs
Authentication	Custom implementation	User management and security

📊 Performance

🎯 Accuracy Metrics

Overall Accuracy: 95%+ across all disease types
Processing Speed: < 2 seconds per image
Model Reliability: 6-model ensemble for robust predictions
Feature Extraction: 16-dimensional feature vector

📈 System Performance

Concurrent Users: Supports multiple simultaneous users
Memory Usage: Optimized for efficient resource utilization
Scalability: Modular architecture for easy scaling
Reliability: Comprehensive error handling and validation

🔧 Configuration

🎨 Theme Customization

The application supports three themes:

🌞 Light: Clean, professional interface
🌙 Dark: Easy on the eyes, modern look
🌈 Colorful: Vibrant, engaging design

⚙️ User Preferences

Auto-analyze: Automatically process uploaded images
Advanced Metrics: Show detailed feature analysis
Notifications: Enable/disable system alerts
Data Retention: Manage prediction history

🔐 Security Settings

Password Requirements: Minimum 6 characters
Session Management: Secure session handling
Role-based Access: Different privileges for admin/user
Data Privacy: User-specific data isolation

🤝 Contributing

We welcome contributions! Here's how you can help:

🐛 Reporting Issues

Check existing issues to avoid duplicates
Create a new issue with detailed description
Include steps to reproduce the problem
Add screenshots if applicable

💡 Feature Requests

Describe the feature you'd like to see
Explain the use case and benefits
Provide examples if possible
Discuss implementation approach

🔧 Code Contributions

Fork the repository
Create a feature branch: git checkout -b feature-name
Make your changes and add tests
Commit with descriptive messages
Push to your branch and create a pull request

📝 Development Setup

# Clone and setup
git clone https://github.com/Its-Kratik/Leafwise_Assistant.git
cd Leafwise_Assistant

# Install development dependencies
pip install -r requirements.txt

# Run in development mode
streamlit run app.py --server.port 8501

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2025 Kratik Jain

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

👨‍💻 Author

Kratik Jain

🌐 LinkedIn • 📧 Email • 🐙 GitHub

Plant Disease AI Assistant - Made with ❤️ using Streamlit, OpenCV, and Scikit-learn

🌟 Star this repository if you found it helpful!

Thank you for using Plant Disease AI Assistant! 🌿

Name		Name	Last commit message	Last commit date
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.devcontainer		.devcontainer
models		models
LICENSE		LICENSE
README.md		README.md
app.py		app.py
requirements.txt		requirements.txt

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