Precision Home Agriculture and Refined Market Evaluation Resource
A comprehensive smart farming system developed as a capstone project, integrating IoT, mobile applications, and machine learning to enable precision agriculture for home and small-scale farming operations.
P.H.A.R.M.E.R is an intelligent agricultural automation platform that combines real-time sensor monitoring, automated actuation, mobile control, and predictive analytics to optimize crop growth and yield. The system features a multi-tier architecture with embedded controllers, a cross-platform mobile app, and a web-based ML platform for data-driven farming decisions.
- ESP32 Microcontroller: Primary controller with WiFi capabilities
- Arduino Pro Mini: Secondary controllers for growth shelf management
- DHT11 Sensor: Temperature and humidity monitoring
- Capacitive Moisture Sensor: Soil moisture detection
- MOSFETs and Relays: Actuator control circuits
- Logic Level Converters: Safe interfacing between different voltage levels
- C/C++: Embedded programming for ESP32 and Arduino
- PlatformIO: Embedded development environment
- Flutter: Cross-platform mobile application development
- Dart: Programming language for mobile app
- Django: Web framework for ML platform
- Python: ML model development and backend logic
- Scikit-learn: Machine learning algorithms
- Pandas & NumPy: Data processing and analysis
- Firebase: Real-time database and backend services
- ArduinoJson: JSON parsing for embedded systems
- Random Forest: Temperature and rainfall prediction
- Regression Models: Crop yield and value forecasting
- Data Sources: FAO agricultural datasets, historical weather data
- Ibrahim Khalid - Embedded Systems & IoT Architecture
- Usama Raheel - Machine Learning & Data Analytics
- Talha Masood - Mobile Application Development
For detailed reports, schematics, and additional resources, visit the complete project folder.