Topic

Technologies and technical equipment for agriculture and food industry

Volume

Volume 75 / No. 1 / 2025

Pages : 1219-1232

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DESIGN OF IOT-BASED GREENHOUSE MONITORING AND CONTROL SYSTEM USING ADAPTIVE PARTICLE SWARM OPTIMIZED FUZZY PID CONTROLLER AND VISUALIZATION PLATFORM

基于自适应粒子群优化模糊PID控制器和可视化平台的物联网温室监控系统设计

DOI : https://doi.org/10.35633/inmateh-75-100

Authors

Peiyu LIN

College of Agricultural Engineering and Food Science, Shandong University of Technology, Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, Zibo City Key Laboratory of Agricultural Product Safety Traceability

Rui XU

College of Agricultural Engineering and Food Science, Shandong University of Technology, Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, Zibo City Key Laboratory of Agricultural Product Safety Traceability

Haifang WANG

Wangjing Hospital, China Academy of Chinese Medical Sciences

Jingcheng HUANG

College of Agricultural Engineering and Food Science, Shandong University of Technology, Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, Zibo City Key Laboratory of Agricultural Product Safety Traceability

Zhen GUO

College of Agricultural Engineering and Food Science, Shandong University of Technology, Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, Zibo City Key Laboratory of Agricultural Product Safety Traceability

Xia SUN

College of Agricultural Engineering and Food Science, Shandong University of Technology, Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, Zibo City Key Laboratory of Agricultural Product Safety Traceability

Ibrahim A. DARWISH

Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University

(*) Yemin GUO

College of Agricultural Engineering and Food Science, Shandong University of Technology, Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, Zibo City Key Laboratory of Agricultural Product Safety Traceability

(*) Jicheng ZHAO

College of Agricultural Engineering and Food Science, Shandong University of Technology, Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, Zibo City Key Laboratory of Agricultural Product Safety Traceability

(*) Corresponding authors:

gym@sdut.edu.cn |

Yemin GUO

zhaojicheng@sdut.edu.cn |

Jicheng ZHAO

Abstract

Traditional greenhouse management often suffers from slow responsiveness and limited adaptability due to its reliance on manual operations. This study proposes a greenhouse environment monitoring and control system that integrates Internet of Things (IoT) technologies with a fuzzy PID controller optimized through an Adaptive Particle Swarm Optimization (APSO) algorithm. A real-time monitoring platform was developed based on a WebSocket-enabled front-end/back-end separation architecture. Environmental parameters, such as temperature and humidity, were collected by sensors and transmitted in real time to the platform via the MQTT protocol, enabling data visualization and anomaly detection. The APSO algorithm was employed offline to optimize the fuzzy PID parameters, and the resulting controller was implemented on a microcontroller to achieve real-time control. Compared with conventional PID control, the APSO-optimized controller reduced overshoot by 72.1% and shortened the settling time by 20%. Experimental results demonstrated that the system was less susceptible to external environmental disturbances, maintaining temperature fluctuations within 0.3°C. This study provides a robust and effective solution for smart greenhouse management.

Abstract in Chinese

由于依赖人工操作，传统的温室管理往往存在响应速度慢、适应性有限等问题。本研究提出了一种温室环境监测和控制系统，该系统将物联网（IoT）技术与通过自适应粒子群优化（APSO）算法优化的模糊 PID 控制器相结合。提出基于 WebSocket 的前端/后端分离架构，开发了一个实时监控平台。温度和湿度环境参数由传感器收集，并通过 MQTT 协议实时传输到平台，从而实现数据可视化和异常检测。采用 APSO 算法离线优化模糊 PID 参数，并在微控制器上实现控制器的实时控制。与传统的 PID 控制相比，APSO 优化控制器将过冲降低了 72.1%，并将稳定时间缩短了 20%。实验结果表明，该系统不易受外部环境干扰的影响，能将温度波动保持在 0.3°C 以内。本研究为智能温室管理提供了一个稳健有效的解决方案。

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