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Horticulturae
Special Issues
Trends of Horticultural Production and Practice in Intelligent Greenhouse
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Trends of Horticultural Production and Practice in Intelligent Greenhouse

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Protected Culture".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 5263

Special Issue Editor

Prof. Dr. Hyo Gil Choi

E-Mail Website
Guest Editor
Department of Horticultural Sciences, Kongju National University, 54, Daehak-ro, Yesan-gun, Chungcheongnam-do 32439, Korea
Interests: plant physiology; greenhouse; artificial light; hydroponics; environment control; big data; phenotyping; yield; growth modeling, vegetable

Special Issue Information

Dear Colleagues,

As a technology to improve the productivity of horticultural crops, greenhouse cultivation has become an incredibly important method. Greenhouse cultivation started with rainproof cultivation in the initial stage and developed to the stage of controlling temperature and humidity and, more recently, using artificial light sources. In addition, hydroponics, which can efficiently supply nutrients, is an important cultivation technique for growth of crops in a greenhouse. Greenhouse cultivation is ultimately about maximizing productivity by managing to maintain optimal growth conditions for crops.

Environmental conditions in the greenhouse for improving the productivity of horticultural crops have be controlled using a computer, and further studies are underway to move to an automatic control system through big data between growth environmental conditions and yield.

An intelligent greenhouse can be described as a cultivation facility through big data and automatic computer control. This Special Issue will focus on the “Trends of Horticultural Production and Practice in Intelligent Greenhouse”. We welcome new research, reviews, and opinions on all related topics, including greenhouse control through big data, growth modeling, environmental control, artificial light, hydroponics, environmental sensing, and changes in plant phenotyping and yield due to different environmental conditions in a greenhouse for horticultural crop production.

Prof. Dr. Hyo Gil Choi
Guest Editor

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. Horticulturae 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 2200 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

  • greenhouse cultivation
  • intelligent greenhouse
  • environmental conditions

Published Papers (2 papers)

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Research

11 pages, 20679 KiB  
Article
Effect of TiO2 Nanoparticles on the Yield and Photophysiological Responses of Cherry Tomatoes during the Rainy Season
by Hyo Gil Choi
Horticulturae 2021, 7(12), 563; https://doi.org/10.3390/horticulturae7120563 - 9 Dec 2021
Cited by 8 | Viewed by 2101
Abstract
The rainy season occurs mainly from June to July in Korea, and this season causes insufficient ambient light intensity for the growth of cherry tomato in a greenhouse. Titanium dioxide (TiO2), as a photocatalyst, is known to affect photosynthesis in plants. [...] Read more.
The rainy season occurs mainly from June to July in Korea, and this season causes insufficient ambient light intensity for the growth of cherry tomato in a greenhouse. Titanium dioxide (TiO2), as a photocatalyst, is known to affect photosynthesis in plants. This study was carried out to investigate the influence of TiO2 foliar spray application on the yield and photophysiological responses of cherry tomato under low ambient light intensity during the rainy season in a greenhouse. Cherry tomato plants were treated with 100 mg·L−1 TiO2 (T1) or 200 mg·L−1 TiO2 (T2) nanoparticle suspension on 26 June. The control group was not treated with TiO2. In the O–J phase of the OJIP transient under a cloudy day (2 July), the slope in the control and T1 groups rose more sharply than that in the T2 group. Conversely, on a clear day (10 July), the J–I phase of the T2 group sharply increased compared to that of the control and T1 groups. On a cloudy day with low ambient light intensity, the rate of electron transport flux from QA to QB per photosystem II reaction center (ET0/RC) and carbon dioxide (CO2) fixation of TiO2-treated plants were increased compared to those of the control. However, on a clear day of high light intensity, the ET0/RC and CO2 fixation of the T2 group were lower than those of the control and Tl groups. The yield of fruit was increased in the T1 group over that in other treatments. TiO2 treatment reduced the size of the fruit and delayed the ripening time, but greatly increased the fruit hardness. These results suggest that setting the concentration and supply amount of TiO2 nanoparticles suitable for various environmental conditions should be prioritized in order to improve the effect of TiO2 nanoparticles in tomato cultivation. Full article
(This article belongs to the Special Issue Trends of Horticultural Production and Practice in Intelligent Greenhouse)
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15 pages, 7206 KiB  
Article
Development of Growth Estimation Algorithms for Hydroponic Bell Peppers Using Recurrent Neural Networks
by Joon-Woo Lee, Taewon Moon and Jung-Eek Son
Horticulturae 2021, 7(9), 284; https://doi.org/10.3390/horticulturae7090284 - 3 Sep 2021
Cited by 7 | Viewed by 2559
Abstract
As smart farms are applied to agricultural fields, the use of big data is becoming important. In order to efficiently manage smart farms, relationships between crop growth and environmental conditions are required to be analyzed. From this perspective, various artificial intelligence algorithms can [...] Read more.
As smart farms are applied to agricultural fields, the use of big data is becoming important. In order to efficiently manage smart farms, relationships between crop growth and environmental conditions are required to be analyzed. From this perspective, various artificial intelligence algorithms can be used as useful tools to quantify this relationship. The objective of this study was to develop and validate an algorithm that can interpret the crop growth rate response to environmental factors based on a recurrent neural network (RNN), and to evaluate the algorithm accuracy compared to the process-based model (PBM). The algorithms were trained with data from three growth periods. The developed methods were used to measure the crop growth rate. The algorithm consisted of eight environmental variables days after transplanting and two crop growth characteristics as input variables producing weekly crop growth rates as output. The RNN-based crop growth rate estimation algorithm was validated using data collected from a commercial greenhouse. The CropGro-bell pepper model was applied to compare and evaluate the accuracy of the developed algorithm. The training accuracies varied from 0.75 to 0.81 in all growth periods. From the validation result, it was confirmed that the accuracy was reliable in the commercial greenhouse. The accuracy of the developed algorithm was higher than that of the PBM. The developed algorithm can contribute to crop growth estimation with a limited number of data. Full article
(This article belongs to the Special Issue Trends of Horticultural Production and Practice in Intelligent Greenhouse)
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