FISHERIES. MARINE. IOT
Aquaculture Smart Monitoring & Automation System.
Project Overview
Designed an intelligent, automated aquaculture monitoring system for modern and traditional methods like RAS, Biofloc, Raceway, and pond-based setups. The system captures hourly water parameters such as dissolved oxygen, pH, salinity, temperature, and ammonia to control environmental equipment and maximize yield with AI-driven alerts via a mobile app and backend.
HARDWARE | FIRMWARE | MOBILE/WEB APPS | EMS MANUFACTURING
Client Requirement & Objectives
Comprehensive Water Quality Monitoring
The system must monitor multiple parameters—dissolved oxygen, temperature, pH, salinity, and ammonia—every hour to ensure ideal aquaculture conditions.
Automation of Control Systems
Based on sensor readings, the solution should automatically control devices like oxygen pumps, water heaters, coolers, and filtration units.
Mobile App Integration
A dedicated Android/iOS mobile app must visualize real-time data and historical trends, enabling farmers to remotely monitor system health.
AI-Based Alerting System
An intelligent backend should analyze sensor data patterns to generate timely alerts or recommendations for intervention.
System Design & Build
Multi-Sensor Node Architecture
Each sensor node integrates various probes interfaced via ADCs and UART/I2C protocols, calibrated to operate in harsh aquaculture conditions.
Data Logging & Transmission
Sensor readings are stored locally in flash memory with timestamped logs, and uploaded in batches to reduce network strain and improve power efficiency.
Real-Time Syncing via MQTT:
Implemented MQTT-based communication for reliable, lightweight real-time sync between edge devices, cloud backend, and mobile app interfaces.
Automated Control Relay Module
Designed a modular control system to switch ON/OFF pumps, heaters, and other devices based on rule-based logic from the AI backend.
Key Challenges & Solutions
Multi-Sensor Data Handling
Coordinating simultaneous reads from multiple sensors required building a scheduling mechanism to avoid cross-interference and ensure data accuracy.
Reliable Data Storage & Uplink
Flash memory was managed using circular buffer logic, and a retry mechanism was used to ensure data was never lost in case of network failure.
Real-Time Sync Complexity
MQTT protocols with optimized QoS levels ensured efficient, low-latency data transfer even in low-bandwidth environments.
Scalable Backend Architecture
Created a scalable backend using cloud microservices that could handle real-time data ingestion from hundreds of sensor nodes without lag or data loss.
Project Tech Stack
Flutter
Espressif
Android
iOS
React
Django
Python
AWS
Client
Bluetechfins is a smart aquaculture technology company dedicated to delivering precise, reliable, and affordable monitoring solutions—empowering fish and shrimp farmers to optimize production, even in the most remote and challenging environments.
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