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Agronomy
Special Issues
Artificial Intelligence in Greenhouse Environment Modelling and Control
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Artificial Intelligence in Greenhouse Environment Modelling and Control

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 10 December 2025 | Viewed by 782

Special Issue Editors

Prof. Dr. Xiaoming Wei

E-Mail Website
Guest Editor
National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
Interests: intelligent greenhouse environmental control systems with emphasis on energy efficiency optimisation and sustainable agricultural production engineering; bridging the gap between traditional greenhouse management and smart agricultural technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Congcong Sun

E-Mail Website
Co-Guest Editor
Agricultural Biosystems Engineering Group, Wageningen University, 6700 AA Wageningen, The Netherlands
Interests: learning-based control methods, such as reinforcement learning and model predictive control, for optimal, autonomous and applicable climate control in greenhouse production
Dr. Jingxin Yu

E-Mail Website
Guest Editor Assistant
National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
Interests: agricultural big data analytics and AI-based modelling for smart greenhouse systems, with particular emphasis on intelligent resource management and optimisation of water-fertiliser efficiency in controlled-environment agriculture

Special Issue Information

Dear Colleagues,

In response to global food security challenges and climate change pressures, greenhouse agriculture has emerged as a crucial solution for sustainable food production. However, traditional greenhouse systems face significant challenges in balancing energy consumption with optimal crop yields. This Special Issue will explore the integration of artificial intelligence technologies into greenhouse environment control, representing a paradigm shift in agricultural automation. By leveraging advanced AI algorithms, machine learning techniques, and smart sensing technologies, modern greenhouse systems can achieve unprecedented levels of efficiency and productivity. This Special Issue will particularly focus on innovative approaches in intelligent environmental monitoring, predictive climate control, adaptive resource management, and energy optimisation strategies. Through cutting-edge research papers, it will showcase how AI-driven solutions are revolutionizing greenhouse operations while addressing critical challenges in energy efficiency and crop production. This Special Issue will bridge the gap between theoretical advances in AI and practical applications in controlled-environment agriculture.

Prof. Dr. Xiaoming Wei
Guest Editor

Dr. Congcong Sun
Co-Guest Editor

Dr. Jingxin Yu
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in agriculture
  • smart greenhouse systems
  • automated environmental control
  • energy-efficient agriculture
  • machine learning applications
  • crop yield optimisation
  • IoT-based monitoring
  • predictive climate control
  • sustainable protected agriculture
  • intelligent resource management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

24 pages, 8568 KiB  
Article
Calibration and Simulation Analysis of Light, Temperature, and Humidity Environmental Parameters of Sawtooth Photovoltaic Greenhouses in Tropical Areas
by Jian Liu, Qingsen Wu, Yini Chen, Yijie Shi and Baolong Wang
Agronomy 2025, 15(4), 857; https://doi.org/10.3390/agronomy15040857 - 29 Mar 2025
Viewed by 246
Abstract
To investigate the light and temperature environmental parameters of photovoltaic greenhouses in tropical areas, this study adopted experimental measurement and simulation methods to test and simulate the photosynthetically active radiation (PAR), relative temperature and humidity, and other environmental parameters inside and outside two [...] Read more.
To investigate the light and temperature environmental parameters of photovoltaic greenhouses in tropical areas, this study adopted experimental measurement and simulation methods to test and simulate the photosynthetically active radiation (PAR), relative temperature and humidity, and other environmental parameters inside and outside two types of serrated photovoltaic greenhouses in Langheng Village, Yangpu, Hainan. The study aimed to explore the distribution laws of PAR, light transmission rates, and relative humidity and temperature inside and outside double-slope and single-slope photovoltaic greenhouses. The ridges of both types of greenhouses run east to west, with photovoltaic panels arranged on the south-facing slopes, covering 57% of the area. The results show the following: (1) The trends of PAR inside and outside both types of photovoltaic greenhouses were consistent across all seasons, with the annual average values were 164.98 μmol/(m2·s) for double-slope and 127.59 μmol/(m2·s) for single-slope; (2) The annual average light transmission rates were 23.91% for double-slope and 19.17% for single-slope; (3) The average indoor temperatures in both types of greenhouses were higher than outside in all seasons, with a temperature difference ranging between 1 and 3 °C; (4) The indoor relative humidity in both types of greenhouses was higher than outside, with the difference reaching up to 6% during summer and autumn; (5) The annual light transmission rates for both types of greenhouses were simulated using Design Builder. The simulation results were generally consistent with the measured values, with the simulated values being higher overall than the measured ones by an average difference within 5%. In summary, the average light transmission rate of the double-slope photovoltaic greenhouse was 4.74% higher that of the single-slope photovoltaic greenhouse and the PAR was 37.39 μmol/(m2·s) higher than the single-slope. Additionally, the average temperature in the double-slope greenhouse was slightly higher and the relative humidity was slightly lower than that in the single-slope greenhouse. Both types of greenhouses could meet the light, temperature, and humidity requirements for cultivating leafy vegetables in tropical areas. Except for the temperature parameters in summer, the performance of the double-slope photovoltaic greenhouse was also better. The Design Builder simulation results showed little difference to the actual measurements and their trends were also consistent. The light transmission rate of photovoltaic greenhouses can be simulated by setting the overall light transmission coefficient of the light-transmitting roofing materials. Full article
(This article belongs to the Special Issue Artificial Intelligence in Greenhouse Environment Modelling and Control)
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