HydroHarvest: Smart Irrigation System

Overview

HydroHarvest is an IoT-based smart irrigation system designed to optimize water usage in agriculture through real-time soil moisture monitoring, weather forecasting, and machine learning algorithms. This system aims to support sustainable agriculture practices by maximizing water efficiency and minimizing waste.

Features

• Soil Moisture Sensing: Utilizes in-ground sensors to monitor soil moisture levels in real-time.
• Weather Forecast Integration: Fetches and analyzes local weather data to predict watering needs.
• Machine Learning Optimization: Employs machine learning algorithms to determine the most efficient watering schedules based on historical data.
• Remote Monitoring & Control: Offers a web-based dashboard and mobile app for users to monitor system status and manually adjust watering schedules as needed.

Components

• Soil Moisture Sensors
• Microcontroller (ESP32/Arduino)
• Relay Modules for Valve Control
• Weather Forecast API
• Cloud Server for Data Analysis and Storage
• User Interface (Web Dashboard/Mobile App)

Setup Instructions

1. Hardware Setup:

   • Install soil moisture sensors in the field and connect them to the microcontroller.
   • Setup the relay module to control the irrigation valves, connecting them to the microcontroller.

2. Software Configuration:

   • Flash the microcontroller with the provided firmware (microcontroller_firmware.py).
   • Deploy the machine learning model to the cloud server (ml_model_example.py).
   • Host the web dashboard on a web server and ensure it's communicating with the cloud backend.

3. System Calibration:

   • Calibrate soil moisture sensors according to the soil type and crop requirements.
   • Test the irrigation system to ensure proper operation of valves and pumps.

Usage

• Monitoring: Access the web dashboard or mobile app to view real-time data on soil moisture levels and weather forecasts.
• Manual Control: Override automatic watering schedules directly from the web dashboard or mobile app.
• Scheduling: Configure the machine learning model to optimize watering schedules based on your specific crop and soil requirements.

Contributing

We welcome contributions from the community, including bug fixes, improvements to the machine learning algorithm, and enhancements to the user interface. Please see our contribution guidelines for more details.

License

HydroHarvest is provided under a proprietary license. Redistribution and use in source and binary forms, with or without modification, are not permitted without explicit permission from the copyright owner. See the LICENSE file for more details.

Acknowledgments

• Weather data provided by Weather API.
• Machine learning model powered by SciKit-Learn.

Contact

For support or questions, please contact us at androidextractions@gmail.com

Disclaimer

This project is provided as-is, and the use of the system is at your own risk. The creators are not liable for any damage or loss resulting from the use of HydroHarvest.