FISHERIES. MARINE. IOT
Aquaculture Smart Monitoring & Automation System.
HARDWARE | FIRMWARE | MOBILE/WEB APPS | EMS MANUFACTURING
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.
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.
Project Tech Stack
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..
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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