IoT-Based Smart Irrigation System

Project Overview

This project is an IoT-based Smart Irrigation System that allows monitoring and control of environmental factors such as temperature, humidity, and soil moisture in real-time. The system is built using NodeMCU, various sensors (humidity, soil moisture, temperature), and Firebase Realtime Database for data storage and retrieval. The data collected from the sensors is visualized in a Flutter-based mobile application, providing users with insights into the environmental conditions of their agricultural fields or gardens.

Additionally, the application integrates a Support Vector Machine (SVM) model to predict irrigation needs based on the sensor data and external weather conditions. A visual meter in the app shows the irrigation level, helping users make informed decisions about watering.

Features

• Real-time Monitoring: Continuously monitors temperature, humidity, and soil moisture levels.
• Data Visualization: Historical and current sensor data are visualized in charts and statistical summaries within a Flutter app.
• SVM-Based Prediction: An SVM model predicts the irrigation requirement based on sensor data and weather conditions.
• Irrigation Level Meter: A visual meter displays the predicted irrigation level, guiding users on when and how much to irrigate.
• Multi-Unit Support: Monitor multiple units or sections of a field with distinct sensor setups.
• Firebase Integration: Sensor data is stored and retrieved from Firebase Realtime Database, ensuring data is accessible from anywhere.
• User-Friendly UI: A Flutter-based app with an intuitive UI for easy interaction and data interpretation.

Project Structure

Hardware Components

• NodeMCU: Microcontroller used for connecting the sensors to the internet.
• DHT11/DHT22 Sensor: For measuring temperature and humidity.
• Soil Moisture Sensor: For measuring the soil moisture content.
• Relay Module: For controlling the water pump based on the soil moisture level.
• Power Supply: To power the NodeMCU and sensors.

Software Components

1. NodeMCU Firmware (Arduino IDE):

   • The NodeMCU is programmed using the Arduino IDE.
   • Connects to the Wi-Fi and sends sensor data to Firebase Realtime Database.
   • Fetches data from DHT11/DHT22 (temperature and humidity) and soil moisture sensor.
   • Controls the water pump based on soil moisture levels.

2. Firebase Realtime Database:

   • Stores sensor data in real-time.
   • Provides a central location for data storage, accessible from the Flutter app.

3. Flutter Mobile Application:

   • AnalyticsPage: A dashboard that visualizes temperature, humidity, and soil moisture data.
   • SVM-Based Prediction: Integrates a Support Vector Machine (SVM) model to predict the irrigation requirement.
   • Irrigation Level Meter: Displays the predicted irrigation level based on current and forecasted weather conditions.
   • Real-time Updates: The app listens for changes in the Firebase database and updates the UI accordingly.
   • Multi-Unit Support: Users can select different units (fields or sections) to monitor.

SVM Model Integration

The SVM model in the app predicts whether irrigation is needed based on sensor data and weather conditions. The prediction is binary (yes/no) or categorical (low, medium, high) depending on the model's training. The prediction is then reflected in the app's UI, guiding the user to make informed irrigation decisions.

Model Features:

• Input Variables:

  • Soil Moisture Level
  • Ambient Temperature
  • Humidity Level
  • External Weather Conditions (e.g., Rain forecast, Cloud cover)

• Output:

  • Predicted irrigation requirement level.

• Visualization:

  • The app displays a meter (like a speedometer) that visually represents the predicted irrigation level. The meter's color and position change based on the SVM model's prediction, making it easy for users to understand the current irrigation needs.

Irrigation Level Meter

• The Irrigation Level Meter is a visual gauge that provides a quick view of the recommended irrigation level.
• Green Zone: Indicates optimal moisture levels—no immediate irrigation needed.
• Yellow Zone: Indicates moderate need—consider irrigation based on future weather conditions.
• Red Zone: Indicates a high need for irrigation—immediate action required.

The meter dynamically adjusts based on:

• Sensor Data: Real-time values of soil moisture, temperature, and humidity.
• Weather Forecast: Predicts future weather conditions (e.g., rain, sun) that could affect irrigation needs.

Getting Started

Prerequisites

• Hardware:

  • NodeMCU (ESP8266)
  • DHT11/DHT22 Sensor
  • Soil Moisture Sensor
  • Relay Module
  • Water Pump (optional)
  • Jumper Wires
  • Breadboard
  • Power Supply

• Software:

  • Arduino IDE
  • Flutter SDK
  • Firebase Account
  • Android Studio (or any IDE for Flutter development)
  • SVM Model (trained using relevant sensor data and weather conditions)

Hardware Setup

1. Connect the DHT11/DHT22 Sensor to the NodeMCU:

   • VCC to 3.3V
   • GND to GND
   • Data Pin to D4 (or any other GPIO pin)

2. Connect the Soil Moisture Sensor to the NodeMCU:

   • VCC to 3.3V
   • GND to GND
   • Analog Output Pin to A0

3. Relay Module and Water Pump:

   • Connect the relay module to control the water pump.
   • VCC to 3.3V
   • GND to GND
   • IN to D1 (or any other GPIO pin)

4. Power the NodeMCU using a USB cable or external power supply.

Software Setup

1. Arduino IDE

• Install the ESP8266 board in Arduino IDE.
• Install necessary libraries:
  • FirebaseESP8266
  • DHT sensor library
• Upload the firmware code to NodeMCU.

#include <ESP8266WiFi.h>
#include <FirebaseESP8266.h>
#include <DHT.h>

#define FIREBASE_HOST "your-firebase-app.firebaseio.com"
#define FIREBASE_AUTH "your-database-secret"
#define WIFI_SSID "your-SSID"
#define WIFI_PASSWORD "your-PASSWORD"
#define DHTPIN D4
#define DHTTYPE DHT11

DHT dht(DHTPIN, DHTTYPE);
FirebaseData firebaseData;

void setup() {
  Serial.begin(115200);
  WiFi.begin(WIFI_SSID, WIFI_PASSWORD);

  while (WiFi.status() != WL_CONNECTED) {
    delay(500);
    Serial.print(".");
  }

  Firebase.begin(FIREBASE_HOST, FIREBASE_AUTH);
  dht.begin();
}

void loop() {
  float h = dht.readHumidity();
  float t = dht.readTemperature();
  int soilMoistureValue = analogRead(A0);

  Firebase.setFloat(firebaseData, "/your-unit-id/temperature", t);
  Firebase.setFloat(firebaseData, "/your-unit-id/humidity", h);
  Firebase.setInt(firebaseData, "/your-unit-id/soilMoisture", soilMoistureValue);

  delay(2000);
}

2. Firebase Setup

• Create a Firebase project.
• Set up Firebase Realtime Database.
• Enable authentication (optional, for securing database access).
• Add your Firebase credentials to the NodeMCU code.

3. Flutter Mobile Application

1. Setup Firebase in Flutter:
   • Add firebase_core, firebase_database, provider, and syncfusion_flutter_charts dependencies in pubspec.yaml.
   • Initialize Firebase in your Flutter app.

dependencies:
  flutter:
    sdk: flutter
  firebase_core: ^1.10.6
  firebase_database: ^9.1.7
  provider: ^6.0.3
  syncfusion_flutter_charts: ^20.3.58
  intl: ^0.17.0

2. Integrate SVM Model:

   • Train an SVM model using historical sensor data and weather conditions.
   • Integrate the trained model into the Flutter app.
   • Use the model's predictions to update the Irrigation Level Meter in the app.

3. Run the Flutter App:

   • Ensure your Android/iOS device is connected.
   • Run flutter pub get to install dependencies.
   • Start the app using flutter run.

Usage

• Monitor Data: Open the app to monitor real-time temperature, humidity, and soil moisture data.
• Switch Units: Use the dropdown in the app to switch between different units (fields or sections) if you have multiple setups.
• SVM Predictions: View the predicted irrigation needs based on sensor data and weather conditions.
• Irrigation Level Meter: Use the meter to guide irrigation decisions, ensuring optimal soil moisture levels.

Folder Structure

.
├── android
├── ios
├── lib
│   ├── utils
│   │   ├── prefs.dart      # Shared preferences management
│   │   ├── shared.dart     # Shared utility functions
│   │   └── stat_card.dart  # UI widget for statistics display
│   ├── analytics_page.dart #

 Main page for analytics
│   ├── svm_model.dart      # SVM model integration
│   ├── meter_widget.dart   # UI widget for Irrigation Level Meter
│   └── main.dart           # Entry point of the Flutter app
├── test
├── pubspec.yaml            # Flutter project dependencies
└── README.md               # Project documentation

Troubleshooting

• Firebase Connection Issues: Ensure the Firebase database URL and authentication key are correctly set in both the NodeMCU and Flutter app.
• Sensor Data Not Displaying: Double-check the wiring of your sensors and ensure they are correctly connected to the NodeMCU.
• App Crashes: Review the Flutter console logs for any error messages and ensure all dependencies are correctly installed.
• SVM Model Predictions: If the model predictions seem inaccurate, retrain the model with updated data and ensure the input features are correctly pre-processed.

Future Enhancements

• Add Notifications: Notify users when the soil moisture is below a certain threshold or when irrigation is needed.
• Automated Irrigation: Implement automatic control of the water pump based on the SVM model's predictions.
• Data Export: Provide options to export data for further analysis.
• Remote Control: Allow remote control of the irrigation system from the app.

License

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