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Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning

Cangqing Wang, Jiangchuan Gong

TL;DR

This work proposes an IoT- and machine learning–driven intelligent greenhouse control system that continuously monitors internal greenhouse conditions and regulates them to boost crop growth efficiency and reduce resource waste. It integrates sensor networks, cloud-based data processing, and ML-driven predictions with a suite of real-time control strategies (PID, MPC, and fuzzy control) to adapt to different crops and environmental changes. Key contributions include a robust data processing pipeline with anomaly detection, an RNN-based forecasting framework, and integrated control policies that optimize energy, water, and fertilizer use. The approach demonstrates practical potential for sustainable, scalable precision agriculture by combining modular hardware design, secure data handling, and adaptive regulation informed by historical and real-time data.

Abstract

This study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. Through meticulous monitoring of intrinsic environmental parameters within the greenhouse and the integration of machine learning algorithms, the conditions within the greenhouse are aptly modulated. The envisaged outcome is an enhancement in crop growth efficiency and yield, accompanied by a reduction in resource wastage. In the backdrop of escalating global population figures and the escalating exigencies of climate change, agriculture confronts unprecedented challenges. Conventional agricultural paradigms have proven inadequate in addressing the imperatives of food safety and production efficiency. Against this backdrop, greenhouse agriculture emerges as a viable solution, proffering a controlled milieu for crop cultivation to augment yields, refine quality, and diminish reliance on natural resources [b1]. Nevertheless, greenhouse agriculture contends with a gamut of challenges. Traditional greenhouse management strategies, often grounded in experiential knowledge and predefined rules, lack targeted personalized regulation, thereby resulting in resource inefficiencies. The exigencies of real-time monitoring and precise control of the greenhouse's internal environment gain paramount importance with the burgeoning scale of agriculture. To redress this challenge, the study introduces IoT technology and machine learning algorithms into greenhouse agriculture, aspiring to institute an intelligent agricultural greenhouse control system conducive to augmenting the efficiency and sustainability of agricultural production.

Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning

TL;DR

This work proposes an IoT- and machine learning–driven intelligent greenhouse control system that continuously monitors internal greenhouse conditions and regulates them to boost crop growth efficiency and reduce resource waste. It integrates sensor networks, cloud-based data processing, and ML-driven predictions with a suite of real-time control strategies (PID, MPC, and fuzzy control) to adapt to different crops and environmental changes. Key contributions include a robust data processing pipeline with anomaly detection, an RNN-based forecasting framework, and integrated control policies that optimize energy, water, and fertilizer use. The approach demonstrates practical potential for sustainable, scalable precision agriculture by combining modular hardware design, secure data handling, and adaptive regulation informed by historical and real-time data.

Abstract

This study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. Through meticulous monitoring of intrinsic environmental parameters within the greenhouse and the integration of machine learning algorithms, the conditions within the greenhouse are aptly modulated. The envisaged outcome is an enhancement in crop growth efficiency and yield, accompanied by a reduction in resource wastage. In the backdrop of escalating global population figures and the escalating exigencies of climate change, agriculture confronts unprecedented challenges. Conventional agricultural paradigms have proven inadequate in addressing the imperatives of food safety and production efficiency. Against this backdrop, greenhouse agriculture emerges as a viable solution, proffering a controlled milieu for crop cultivation to augment yields, refine quality, and diminish reliance on natural resources [b1]. Nevertheless, greenhouse agriculture contends with a gamut of challenges. Traditional greenhouse management strategies, often grounded in experiential knowledge and predefined rules, lack targeted personalized regulation, thereby resulting in resource inefficiencies. The exigencies of real-time monitoring and precise control of the greenhouse's internal environment gain paramount importance with the burgeoning scale of agriculture. To redress this challenge, the study introduces IoT technology and machine learning algorithms into greenhouse agriculture, aspiring to institute an intelligent agricultural greenhouse control system conducive to augmenting the efficiency and sustainability of agricultural production.
Paper Structure (49 sections, 9 equations, 4 figures, 3 tables)

This paper contains 49 sections, 9 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Greenhouse internal environmental monitoring system
  3. Sensor selection and layout: temperature, humidity, light and other parameters
  4. Sensor Selection
  5. Sensor Layout
  6. Data acquisition and transmission: real-time monitoring of the greenhouse internal environment
  7. Data Collection
  8. Data Transfer
  9. Data processing and storage: Cloud computing applications
  10. Data processing
  11. Anomaly Detection and Repair During Data Processing
  12. Application of machine learning algorithm
  13. Application of machine learning algorithms
  14. Data analysis and model training: The use of historical data
  15. Data Preparation
  16. ...and 34 more sections

Figures (4)

  • Figure 1: The structure diagram of the intelligent agricultural temperature control system of the Internet of Things
  • Figure 2: Circuit diagram of light source module
  • Figure 3: Measurement results using the smart agricultural system (Air Humidity, Soil Moisture (left)and Soil Temperatire, Air Temperature (right))
  • Figure 4: Measurement results using the smart agricultural system (Soil pH value, Light Intensity (left) and Air Pressure, Wind speed (right))