💧 IoT-Based Smart Water Quality Monitoring System

📖 Overview

The IoT-Based Smart Water Quality Monitoring System measures and monitors water parameters including Turbidity, pH, TDS, NPK levels, Gas presence, and Temperature using Arduino and ESP32 modules. The data is sent to a cloud server and visualized on a web/mobile dashboard, enabling real-time water quality monitoring for farmers, environmental agencies, and industries.

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🎯 Problem Statement

Manual water testing is time-consuming, costly, and prone to errors. This project automates water quality monitoring by:

• Providing real-time data collection and analysis.
• Reducing manual intervention.
• Sending data to cloud for easy access and decision-making.

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⚙️ Tech Stack

Component	Technology Used
Microcontroller	Arduino Uno / ESP32
Cloud Platform	Thingspeak / AWS IoT / Firebase
Sensors	Turbidity, pH, TDS, NPK, Gas (MQ-series), Temperature
Communication	Wi-Fi (ESP32) / Serial
Database	Cloud database (MySQL / Firebase Realtime DB)
Frontend Dashboard	Web / Mobile App
Language	C/C++ (Arduino IDE)

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🧰 Components Required

Component	Description
Arduino Uno / ESP32	Main controller for data acquisition and transmission.
Turbidity Sensor	Measures water clarity (NTU).
pH Sensor	Measures acidity/alkalinity.
TDS Sensor	Detects total dissolved solids in ppm.
NPK Sensor	Measures nitrogen, phosphorus, potassium.
MQ Gas Sensor	Detects harmful gases or contamination.
Temperature Sensor	Reads water temperature.
LCD Display	Displays sensor readings locally.
Wi-Fi Module	Sends collected data to cloud.
Power Supply	5V regulated power input.

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🔌 Circuit Connections (Example: Arduino Uno + ESP32)

Sensor	Arduino Pin	Description
Turbidity Sensor	A0	Analog input for turbidity
pH Sensor	A1	Analog input for pH
TDS Sensor	A2	Analog input for TDS
NPK Sensor	A3	Analog input for NPK
MQ Gas Sensor	A4	Detects harmful gases
Temperature Sensor	D2	Digital input
ESP32 / ESP8266	TX/RX	Wi-Fi data transmission
LCD Display	D4–D7	Display output
Power	5V & GND	Common ground & power

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🧠 Working Principle

1. Sensor Data Collection: Each sensor reads its parameter (pH, TDS, Turbidity, etc.).
2. Data Processing: Arduino converts analog values to digital, applies calibration.
3. Data Transmission: ESP32 uploads processed data to cloud via Wi-Fi.
4. Cloud Storage: Data is stored for historical tracking and visualization.
5. Dashboard Visualization: Displays live values, trends, and alerts.

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☁️ Cloud Workflow

[ Sensors ] 
    ↓
[ Arduino / ESP32 ] 
    ↓ (Wi-Fi)
[ Cloud Database (AWS / Firebase / Thingspeak) ] 
    ↓
[ Web / Mobile Dashboard ]

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💾 Example Data Format

Timestamp	Turbidity (NTU)	pH	TDS (ppm)	N (mg/L)	P (mg/L)	K (mg/L)	Gas Level	Temp (°C)	Water Quality
2025-10-25 10:30	2.5	6.8	320	12	5	18	50	28.5	Good
2025-10-25 10:45	6.9	7.2	420	15	8	22	65	29.1	Moderate

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📲 Application Use Flow

Start → Sensors Initialize → Measure Parameters → Send to Cloud → Display Dashboard

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💻 Arduino + ESP32 Setup Instructions

1. Clone the repository

git clone https://github.com/<your-username>/iot-water-quality.git
cd iot-water-quality

2. Open Arduino IDE

• Install required libraries:
  • WiFi.h
  • HTTPClient.h
  • LiquidCrystal.h
  • OneWire.h
  • DallasTemperature.h
  • ThingSpeak.h

3. Connect Hardware

Connect sensors as per circuit table and verify COM port.

4. Update Wi-Fi and Cloud Credentials

const char* ssid = "YourWiFiName";
const char* password = "YourWiFiPassword";
const char* server = "https://your-cloud-endpoint/api/";

5. Upload Code

• Select your board: Arduino Uno / ESP32
• Upload the .ino file.

6. Monitor Serial Output

Check live readings via Serial Monitor.

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🧾 Cloud & Dashboard Setup

• ThingSpeak: Create channel & API key, link ESP32 HTTP POST.
• AWS IoT Core: Create device & topic, upload certificates.
• Firebase: Connect via REST API, visualize in Flutter/web app.

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🚨 Alerts & Thresholds

Parameter	Safe Range	Alert Condition
pH	6.5 – 8.5	<6 or >9
TDS	0 – 500 ppm	>500
Turbidity	0 – 5 NTU	>5
NPK	Optimal nutrient range	Out of range
Gas	<100 ppm	>150 ppm

Alerts can trigger buzzers or SMS/email notifications.

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📈 Dashboard Example

• Real-time readings
• Graphs for each sensor
• Water quality classification
• Historical reports

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📸 Screenshots

Iot Sensors Application Working Dashboard

The Working Procedure Video Link

https://youtube.com/shorts/c4t2vuDY0r4?si=7bTsnMxe-LTXeFo6

🚀 Future Enhancements

• AI-based contamination detection
• Automated water treatment control (valves/pumps)
• Solar-powered IoT module
• Mobile app integration with Flutter
• Voice-based water quality alerts

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👨‍💻 Contributors

Name	Role	Description
S. Chandu	Lead Developer & IoT Engineer	Designed sensor circuit, coded Arduino + ESP32, integrated cloud APIs.

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📄 License

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

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Contact

If you have any questions or suggestions regarding the Coffee app, feel free to contact us at kingchandus143@gmail.com

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