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# Optimizing Land-Based Fish Feeding with Node-RED

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.

- **Speakers:** [Øivind Heggland](https://www.wearedevelopers.com/@oivind-heggland)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 2, 2026
- **Duration:** 36:10
- **URL:** https://www.wearedevelopers.com/videos/2032-optimizing-land-based-fish-feeding-with-node-red

## Summary

The video details the architectural overhaul of a land-based fish feeding system, transitioning from an opaque, legacy PLC-driven setup to a transparent, edge-first infrastructure using Node-RED and Raspberry Pi. Managing thousands of fish across 500+ active feeders required migrating critical execution logic to the edge (AquaFeeder) while centralizing logistics and scheduling in a master coordinator (AquaMaster). This separation of responsibilities ensures that if the central network or master node fails, the edge devices can safely maintain their schedules or operators can seamlessly fall back to manual operation.

By leveraging Node-RED as a bridge between proprietary Operational Technology (OT) protocols like Modbus and modern IT frameworks like MQTT, the system successfully abstracts hardware constraints. The logistical core operates on an industrial mini-PC handling chain conveyors and silos, feeding state data to a Blazor .NET user interface. Meanwhile, individual Raspberry Pi units at the edge execute independent feeding schedules using Standard Growth Rate parameters. To resolve native hardware dependencies, such as differences in GPIO libraries across Raspberry Pi versions 3 through 5, the team isolated GPIO triggers into an independent MQTT service, ensuring cross-platform compatibility.

Because robust systems must anticipate physical hardware failure, the architecture includes an automated recovery flow to handle notorious SD card corruptions. Operators simply swap in a preloaded SD card, which automatically connects via MQTT to the AquaMaster, retrieves its MAC address, and self-provisions its historical configuration without technical intervention. Furthermore, containerizing the Node-RED application and deploying via Azure DevOps has modernized fleet management, shifting the team from manual, site-by-site SD card replacements to remote Docker updates. Ultimately, the system demonstrates that while triggering hardware actions is straightforward, engineering the safety parameters for "when not to do something" remains the most complex and vital component of industrial control design.

**Keywords:** land-based fish farming automation, node-red industrial IOT, edge-first architecture, legacy PLC modernization, raspberry pi fleet management, MQTT hardware abstraction, modbus to MQTT bridging, automated SD card recovery, docker containerization for IOT, azure devops IOT pipelines, OT and IT protocol integration, blazor .NET IOT dashboard, industrial control systems fallback, automated feeding logistics, yaskawa frequency driver control, remote edge device updates

## Chapters

1. **Land-based fish farming standalone feeder overview** (00:00) — The physical feeder automates continuous feeding without user input using tables and temperature parameters.
1. **Hardware components of the automated fish feeder** (00:39) — The system integrates a Yaskawa frequency driver, UPS card, and a Raspberry Pi to manage motor speed and power fluctuations.
1. **Raspberry Pi and Modbus for feeder control** (01:52) — A Raspberry Pi calculates feed rates and controls the Yaskawa driver over Modbus using a dedicated USB Ethernet connection.
1. **Replacing the legacy PLC with Node-RED** (03:15) — A legacy proprietary PLC was replaced with remote I/O modules and logic ported into Node-RED to regain control over the system.
1. **Scaling hardware to overcome resource constraints** (05:48) — Upgrading from underpowered Raspberry Pis to Lenovo mini PCs and IOThings 4510 modules provided the necessary resources for complex logistics.
1. **Physical logistics of transporting fish feed pellets** (07:57) — Feed is moved from silos to weighing stations and transported via chain conveyors out to individual tank hoppers.
1. **Separating responsibilities between feeder and master roles** (10:25) — The system splits duties between calculating individual feed rates at the tank and managing bulk feed transport logistics centrally.
1. **Network topology for distributed fish feeding nodes** (12:47) — A layered network using managed switches and RSTP provides resilient connectivity across multiple physical feed rings.
1. **Using Node-RED flows and debug nodes** (14:16) — Core feeding logic is packed into a JSON flow where debug nodes simplify monitoring payload messages in real time.
1. **Automating hardware recovery after SD card failures** (16:52) — A dedicated provisioning flow automatically retrieves configuration from the master when operators swap corrupted SD cards.
1. **Interval logic and abstracting GPIO via MQTT** (19:36) — Containerized Node-RED communicates with GPIO pins through an MQTT service to maintain compatibility across different Raspberry Pi versions.
1. **Managing logistics queues with Node-RED subflows** (21:28) — The core Aquamaster relies on numerous flows and an internal MQTT broker to route commands between the UI and automated weights.
1. **Developing with containers and a hardware simulator** (25:37) — Developing inside Node-RED containers alongside a .NET simulator ensures seamless integration testing before deploying updates via Azure DevOps.
1. **Evaluating Node-RED for industrial production environments** (28:24) — While providing visual programming and instant deployments, scaling Node-RED across large teams may require enterprise wrappers like Flowfuse.
1. **Overcoming challenges with fleet updates and testing** (30:45) — As facilities scale up, manual SD card swaps are being replaced by automated fleet updates and container-based remote deployments.
1. **Key lessons for building edge control systems** (33:05) — 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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