Smart acqaculture monitoring system aiot.pdf

Smart acqaculture monitoring system

3. Introduction
•Aquacultureis the controlled cultivation of fish and other aquatic organisms.
•Plays a key role in ensuring food securityand economic growth.
•Water quality parameters like temperature, pH, dissolved oxygen, and turbiditydirectly
affect fish health.
•Manual monitoringis laborious, slow, and prone to errors.
•Wireless Sensor Network (WSN)-based monitoringenables real-time, accurate, and
automatedtracking of water quality.
•Integration with IoT and cloud platformshelps farmers make data-driven decisionsfor better
productivity and sustainability.

4. Applications
•Real-time water quality monitoringin fish and shrimp farms.
•Automated controlof aerators, feeders, and pumps based on sensor
data.
•Early detectionof environmental changes to prevent fish diseases.
•Data logging and analyticsfor long-term farm management and
optimization.
•Remote monitoringvia cloud dashboards and mobile apps.
•Useful in ponds, tanks, and offshore aquaculture systems.
•Supports smart and sustainable aquaculture practices.

5. Objectives
•To design a WSN-based systemfor real-time monitoring of aquaculture water
quality.
•To measure key parameterssuch as temperature, TDS, and turbidity.
•To develop a low-cost, energy-efficient, and scalablemonitoring solution.
•To enable wireless data transmissionand cloud-based data storage.
•To provide real-time alertsfor abnormal water conditions.
•To enhance farm productivityand promote sustainable aquaculturepractices

6. Proposed Methodology
•Step 1:Identify key water quality parameters —Temperature, TDS, and
Turbidity.
•Step 2:Interface sensors (DS18B20, TDS, Turbidity) with ESP32
microcontroller.
•Step 3:Use LoRa/Wi-Fifor wireless data transmission.
•Step 4:Send real-time data to cloud platforms(e.g., Firebase).
•Step 5:Display live readings and trends on a web or mobile dashboard.
•Step 6:Trigger alertswhen values cross threshold limits.
•Step 7:Power system using solar energy and Li-ion batteryfor continuous
operation.

7. IoT Level Justification
•The system fits under IoT Level 3–Cloud-based Data Monitoring and Analysis.
•Reason:
•Multiple sensor nodes(Temperature, TDS, Turbidity) collect real-time data.
•Data is transmitted via ESP32using Wi-Fi/LoRato a cloud server.
•Cloud platform (Firebase)stores and processes sensor data.
•Dashboard interfaceallows users to monitor and analyze water quality remotely.
•Supports real-time alerts, historical data trends, and decision-making.
•Hence, it represents a cloud-centric IoT architecturefocused on monitoring and analytics.

8. Hardware Requirements
•Microcontroller:ESP32 (with inbuilt Wi-Fi and Bluetooth)
•Sensors:
•DS18B20 –Temperature Sensor
•TDS Sensor –Total Dissolved Solids measurement
•Turbidity Sensor –Water clarity detection
•Communication Module:LoRa transceiver (for long-range, low-power data transfer)
•Power Supply:
•Solar panel for renewable power
•Li-ion battery with charge controller
•Additional Components:
•Voltage regulator (LM2596)
•Connecting wires, waterproof enclosures, cables, and mounting units
•Optional:Raspberry Pi or LoRa Gateway for cloud connectivity

9. Software Requirements
•Programming Environment:
•Arduino IDE / PlatformIO(for ESP32 firmware)
•Languages Used:
•C / C++ for embedded programming
•HTML, CSS, JavaScript (for dashboard development)
•Python (for backend or data visualization, if used)
•Cloud Platform:
•Firebase Realtime Database / Blynk / ThingsBoard
•Communication Protocol:
•MQTT (for efficient IoT data transfer)
•Libraries:
•WiFi.h, PubSubClient.h, OneWire.h, DallasTemperature.h, LoRa.h
•Additional Tools:
•Node-RED or Flask (for dashboard and alert system)
•