World Congress 2026 Europe - Virtual Stage Jul 2, 2026 Session details

Optimizing Land-Based Fish Feeding with Node-RED

Øivind Heggland

Triggering industrial hardware is easy, but engineering when not to act is the real challenge. Discover how an edge-first Node-RED architecture brings resilient, self-healing orchestration to legacy systems.

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#1 about 1 min

Land-based fish farming standalone feeder overview

The physical feeder automates continuous feeding without user input using tables and temperature parameters.

#2 about 2 min

Hardware components of the automated fish feeder

The system integrates a Yaskawa frequency driver, UPS card, and a Raspberry Pi to manage motor speed and power fluctuations.

#3 about 2 min

Raspberry Pi and Modbus for feeder control

A Raspberry Pi calculates feed rates and controls the Yaskawa driver over Modbus using a dedicated USB Ethernet connection.

#4 about 3 min

Replacing the legacy PLC with Node-RED

A legacy proprietary PLC was replaced with remote I/O modules and logic ported into Node-RED to regain control over the system.

#5 about 3 min

Scaling hardware to overcome resource constraints

Upgrading from underpowered Raspberry Pis to Lenovo mini PCs and IOThings 4510 modules provided the necessary resources for complex logistics.

#6 about 3 min

Physical logistics of transporting fish feed pellets

Feed is moved from silos to weighing stations and transported via chain conveyors out to individual tank hoppers.

#7 about 3 min

Separating responsibilities between feeder and master roles

The system splits duties between calculating individual feed rates at the tank and managing bulk feed transport logistics centrally.

#8 about 2 min

Network topology for distributed fish feeding nodes

A layered network using managed switches and RSTP provides resilient connectivity across multiple physical feed rings.

#9 about 3 min

Using Node-RED flows and debug nodes

Core feeding logic is packed into a JSON flow where debug nodes simplify monitoring payload messages in real time.

#10 about 3 min

Automating hardware recovery after SD card failures

A dedicated provisioning flow automatically retrieves configuration from the master when operators swap corrupted SD cards.

#11 about 2 min

Interval logic and abstracting GPIO via MQTT

Containerized Node-RED communicates with GPIO pins through an MQTT service to maintain compatibility across different Raspberry Pi versions.

#12 about 5 min

Managing logistics queues with Node-RED subflows

The core Aquamaster relies on numerous flows and an internal MQTT broker to route commands between the UI and automated weights.

#13 about 3 min

Developing with containers and a hardware simulator

Developing inside Node-RED containers alongside a .NET simulator ensures seamless integration testing before deploying updates via Azure DevOps.

#14 about 3 min

Evaluating Node-RED for industrial production environments

While providing visual programming and instant deployments, scaling Node-RED across large teams may require enterprise wrappers like Flowfuse.

#15 about 3 min

Overcoming challenges with fleet updates and testing

As facilities scale up, manual SD card swaps are being replaced by automated fleet updates and container-based remote deployments.

#16 about 4 min

Key lessons for building edge control systems

Putting control logic at the edge and standardizing hardware deployments ensures systems can gracefully fall back to manual operation during failures.

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Navigating hardware constraints and edge computing challenges

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Optimizing latency and cost with free open source software

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2:40 min

Decoupling industrial functionality from physical control hardware

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