An intelligent flower classification system powered by machine learning

FloraMind is a full-stack web app that uses machine learning to classify flowers from user measurements. Powered by the Iris dataset and K-Nearest Neighbors, it predicts species through an intuitive interface.

---

Features

Interactive User Interface

• Clean, responsive Bootstrap 5-based design
• Real-time input validation
• Live measurement preview
• Mobile-friendly interface

Machine Learning Classification

• K-Nearest Neighbors (KNN) algorithm implementation
• Trained on the classic Iris dataset
• Predicts three species: Iris Setosa, Iris Versicolor, Iris Virginica
• High accuracy classification

Dynamic Results Display

• Color-coded species predictions
• Icon-based visual feedback
• Detailed classification confidence scores
• Responsive result cards

Data Persistence

• MySQL database integration
• Automatic prediction logging
• Timestamp tracking
• Historical data storage

Analytics Capabilities

• Prediction history tracking
• Usage statistics
• Model performance insights
• Admin dashboard (optional)

Technology Stack

Layer	Technologies
Frontend	HTML5, CSS3, Bootstrap 5, JavaScript (ES6)
Backend	PHP 8, PHP-ML Library
Database	MySQL 8
ML Algorithm	K-Nearest Neighbors (KNN)
Dataset	Iris Dataset (CSV)

User Guide

Making a Prediction

1. Enter Measurements

   • Sepal Length (cm): 4.0 - 8.0
   • Sepal Width (cm): 2.0 - 5.0
   • Petal Length (cm): 1.0 - 7.0
   • Petal Width (cm): 0.1 - 3.0

2. Submit for Classification

   • Click the "Classify Flower" button
   • Wait for the prediction results

3. View Results

   • Species name displayed
   • Confidence score shown
   • Visual indicators provided

Understanding Results

Iris Setosa

• Typically small petals
• Wide sepals
• Color: Purple theme

Iris Versicolor

• Medium-sized features
• Balanced proportions
• Color: Blue theme

Iris Virginica

• Large petals
• Narrow sepals
• Color: Pink theme

Project Structure

floramind/
├── index.php              # Main application entry (HTML UI)
├── predict.php            # Backend API for predictions
├── script.js              # Frontend logic & AJAX
├── style.css              # Custom styles
├── iris.csv               # Training dataset (150 samples)
├── database/
│   └── schema.sql         # Database schema
├── vendor/                # Composer dependencies
│   └── php-ai/php-ml/     # PHP Machine Learning library
├── composer.json          # Dependency manifest
└── composer.phar          # Local Composer (if installed locally)

How It Works

1. Data Collection

The application uses the Iris dataset, containing 150 samples with four features each:

• Sepal length and width
• Petal length and width

2. Model Training

The KNN algorithm is trained using PHP-ML:

$classifier = new KNearestNeighbors($k = 3);
$classifier->train($samples, $labels);

3. Prediction

User input is processed and classified:

$prediction = $classifier->predict($userInput);

4. Storage

Results are logged to MySQL for analytics:

INSERT INTO predictions (sepal_length, sepal_width, 
petal_length, petal_width, predicted_species, timestamp)
VALUES (?, ?, ?, ?, ?, NOW());

Educational Value

Learning Objectives

• Understanding supervised machine learning
• Implementing KNN algorithm
• Full-stack web development
• Database design and integration
• RESTful API concepts
• Data validation and sanitization

Suitable For

• Computer Science students
• Web development learners
• Machine learning beginners
• Full-stack bootcamp projects

Extending FloraMind

Suggested Enhancements

1. Multi-Model Support: Add SVM, Decision Trees, Random Forest
2. Image Classification: Integrate CNN for flower image recognition
3. User Authentication: Add login and personalized history
4. Real-time Charts: Visualize predictions with Chart.js
5. Export Functionality: Download prediction history as CSV
6. API Rate Limiting: Implement request throttling
7. Caching Layer: Use Redis for faster predictions

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Support

For questions and support:

• 🐛 Issues: GitHub Issues

Roadmap

• Basic KNN implementation
• Web interface
• Database integration
• Analytics dashboard
• Multi-model support
• Docker containerization
• API authentication
• Image classification

---

Built with ❤️ for education and innovation