User Interface

The user interface is a web-based dashboard that visually displays real-time sensor data, including temperature, humidity, light intensity, and soil moisture levels. It offers an intuitive layout with graphs, maps, and alerts for easy monitoring.

• Real-time data visualization
• Interactive graphs for trends
• Map integration for sensor locations
• User-friendly and responsive design

Frontend Technology Stack

The frontend is built using Flutter for its component-based architecture and responsive design. The interface fetches data from Firebase and displays it in real-time with dynamic updates.

• Responsive Design
• Modern UI Components
• Optimized for Speed

Database

Firebase Firestore is used as the cloud database to store real-time sensor data, including timestamps, coordinates, and environmental readings. It provides scalable, NoSQL data storage with automatic syncing.

• Firebase Firestore (NoSQL cloud database)
• Real-time data storage and syncing
• Timestamp-based entries for sensor data
• Scalable and secure

Backend Technology Stack

The backend uses Firebase Functions to process incoming data from ESP32 devices. It handles CRUD operations, ML model integration, and real-time updates to the frontend.

• Firebase Functions for serverless processing
• ESP32 for data collection and transmission
• HTTP & MQTT protocols for data transfer

Sensors and Modules

We chose following sensor according to the required accuracy level.

• ESP32 Microcontroller
• DHT22 Sensor
• Hall Sensor
• Neo-6M Sensor
• Capacitive Soil Moisture Sensor
• NPK Sensor
• BH1750 Light Intensity Sensor
• DC to DC step down module
• Rechargeable Battery

Main Circuit Diagram

Our Main Circuit Diagram illustrates the core components of our Smart Agricultural Monitoring System built around the ESP32 microcontroller. The system integrates multiple sensors, including the DHT22 for temperature and humidity, BH1750 for light intensity, and a soil moisture sensor to assess soil health. The Neo-6M GPS module provides real-time location data, while the hall sensor detects magnetic field changes, useful for monitoring mechanical operations. An LCD display is connected to visually present sensor readings directly. All components are powered by a battery source, with proper wiring ensuring stable communication through I2C (SDA, SCL pins) for the BH1750 and LCD, and GPIO pins for the remaining sensors. This setup enables seamless data collection and transmission via the ESP32, forming the foundation for real-time, remote agricultural monitoring.

Why Testing is necessary?

The main benefit of testing is the identification and subsequent removal of the errors. However, testing also helps developers and testers to compare actual and expected results in order to improve quality. If the software production happens without testing it, it could be useless or sometimes dangerous for customers.

Focuses on ensuring the user interface works seamlessly. This includes verifying correct data visualization, responsive design across devices, and smooth user interactions, ensuring real-time sensor data displays without glitches.

Involves checking the proper functioning of sensors (DHT22, BH1750, soil moisture, GPS), ensuring accurate data collection. Tests include sensor calibration, power stability, and connectivity validation with the ESP32 and LoRa modules.

Trains a machine learning model using collected sensor data to predict trends like soil moisture levels or plant health. Testing ensures the model’s accuracy, checks for overfitting, and validates predictions using real-world data.

Focuses on validating data flow between ESP32 and Firebase Firestore. Tests check for secure data storage, real-time updates, proper authentication, and verifying CRUD (Create, Read, Update, Delete) operations work as expected.