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

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering Pub Date : 2025-10-02 DOI:10.1016/j.compeleceng.2025.110713

Amir Hossein Hooshmand

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Abstract

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.

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智能模块化温室控制通过物联网，LabVIEW和PSO-PID集成

本研究提出了一种用于半工业化温室的动态控制系统，旨在优化水消耗，提高能源效率，并实现精确的实时环境监测。该系统集成了一个密集的传感器网络，包括20个用于定向灌溉的土壤湿度传感器，以及SHT31-D温湿度传感器和TSL2561光传感器，确保准确和分布式的数据采集。驱动是通过模块化继电器基础设施来实现的，该基础设施可以管理泵、风扇、加热装置和照明，并具有可扩展性，包括用于工业应用的附加传感器，如pH值和雨水探测器。采用粒子群优化（PSO）调谐比例-积分-导数（PID）算法增强控制性能。用显式欧拉方法在500秒范围内实现的MATLAB仿真表明，与传统的开关方法相比，能耗降低了25%。通过消息队列遥测传输（MQTT）通信、基于labview的监控仪表板和Delta人机界面（HMI）触摸屏支持远程访问。农民的反馈为即插即用传感器和可配置继电器的设计提供了依据，从而降低了安装的复杂性，提高了可用性。对比分析突出了卓越的响应能力、可扩展性和可持续性。该平台为下一代温室自动化提供了基础，并展示了机器学习集成的强大潜力，为可持续智能农业做出了贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers & Electrical Engineering 工程技术-工程：电子与电气

CiteScore

9.20

自引率

7.00%

发文量

661

审稿时长

47 days

期刊介绍： The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.