Table of Contents Web page
- Project Description
- Features
- Technologies Used
- Existing Structure
- Our Solution
- Usage
- Contributing
- License
- Contact
The Weather Integrated Automated Irrigation System (WIAIS) is an IoT-based project that utilizes weather forecast data to automate and optimize irrigation in various agricultural settings. The system is designed to benefit large-scale agriculture, garden and lawn owners, as well as semi-automated agriculture for specific crops. Unlike existing solutions, our project integrates weather forecasts and remote monitoring and control features using cloud services, providing a more efficient and effective irrigation solution.
- Automated irrigation system based on weather forecasts
- Remote monitoring and control of irrigation system
- Integration of field sensor data and web-scraped weather data
- Mobile and desktop app interfaces for easy access and control
- Hardware Used:
- Raspberry Pi
- Soil Moisture Sensor
- Software Used:
- Python3
- API
- Services Used:
- Firebase (Cloud database service)
The existing system relies on sensors, controllers, and valves to regulate the water delivered to the plants based on their specific needs. The sensors measure soil moisture levels, temperature, humidity, and rainfall, which are then processed by the controller to determine the optimal irrigation schedule. However, the system lacks the ability to anticipate weather conditions before they occur.
Our system addresses the limitations of the existing structure by integrating a Raspberry Pi that periodically checks the current field parameters and incorporates weather forecast data from the web. This allows the system to make informed decisions based on upcoming weather conditions, preventing unnecessary irrigation when precipitation is predicted. Additionally, the entire system can be remotely monitored and controlled through a mobile and desktop app, providing convenience and real-time insights into sensor data and weather information.
The WIAIS system offers several benefits and applications, including:
- Reduction in labor required for irrigation in large-scale agriculture
- Power savings by avoiding unnecessary irrigation during predicted rainfall
- Conservation of water resources
- Customizability for different crops and environmental conditions
- Remote monitoring of field irrigation status and remote control of devices, such as water pumps
- Retrieval of environmental parameters from field geolocation
We welcome contributions to our project. To contribute, please follow the guidelines outlined in our contribution guidelines document here.
This project is released under the Zenith-SSN Verifyed.
For any inquiries or questions regarding the project, feel free to reach out to our team at:
- Email: srikanth2110893@ssn.edu.in
- GitHub: https://github.com/Srikanth-Drklrd
- Email: karthikeyan2110641@ssn.edu.in
- GitHub: https://github.com/KKBUGHUNTER