Smart modular greenhouse control via IoT, LabVIEW, and PSO-PID integration

This study presents a dynamic control system for semi-industrial greenhouses, designed to optimize water consumption, improve energy efficiency, and enable precise real-time environmental monitoring. The system integrates a dense sensor network comprising 20 soil moisture sensors for targeted irrigation, as well as SHT31-D temperature–humidity and TSL2561 light sensors, ensuring accurate and distributed data acquisition. Actuation is achieved through a modular relay-based infrastructure that manages pumps, fans, heating units, and lighting, with scalability to include additional sensors such as pH and rain detectors for industrial applications.

Control performance is enhanced using a Particle Swarm Optimization (PSO)-tuned Proportional–Integral–Derivative (PID) algorithm. MATLAB simulations, implemented with the explicit Euler method over a 500-second horizon, demonstrated a 25 % reduction in energy consumption compared with conventional on–off approaches. Remote access is supported via Message Queuing Telemetry Transport (MQTT) communication, a LabVIEW-based supervisory dashboard, and a Delta Human–Machine Interface (HMI) touchscreen. Farmer feedback informed the design of plug-and-play sensors and configurable relays, reducing installation complexity and improving usability. Comparative analyses highlight superior responsiveness, scalability, and sustainability. The proposed platform provides a foundation for next-generation greenhouse automation and demonstrates strong potential for machine learning integration, contributing to sustainable smart farming.