Special Issue "Future Development Trends of Intelligent Greenhouses"

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: 20 November 2022 | Viewed by 2919

Special Issue Editor

Dr. Oliver Körner
E-Mail Website
Guest Editor
Leibniz Institute of Vegetable and Ornamental Crops, Großbeeren, Germany
Interests: decision support systems; greenhouse technology; crop modeling; microclimate; early stress detection; soft sensors; cultivation monitoring; environmental assessment; controlled environment farming

Special Issue Information

Dear colleagues,

Covering crops and production in protected environments had for a long time mainly been a solution with a plastic or a glass cover, more or less climate controlled. In more advanced systems, options for automated climate regulation with various actuators being controlled by a climate computer are standard. Modern greenhouses are equipped with loads of technology and can be operated regardless of the outside climate conditions and geographical location. The extension of production to cooler seasons and the production in harsh climates enable year-round production. This contributes, on one hand, to climate resilience in crop production, but on the other hand, also to energy consumption for cooling, heating, and supplementary lighting. To tackle these problems, among others, greenhouse technology is evolving and through combinations of hardware components, microsensors, mathematical models, and software, the trend moves to climate, resource, and crop quality/yield-optimized intelligent greenhouse systems. Next to optimizing the relation of crop production and resource consumption, internal logistics and various labor steps are taken over by robots, and in some places, drones could be observed, too. In intelligent greenhouse systems, information from sensors is used for both decision support and computerized climate control, and the first steps toward autonomous greenhouse production have already been taken. In this Special Issue, we address all aspects of progress toward intelligent greenhouses, with systems for production planning, cultivation monitoring and prediction, logistics and labor, as well as optimizing environmental issues with smart sensors or control.

Dr. Oliver Körner
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. Agriculture 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 1800 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

  • climate control
  • smart greenhouse
  • decision support
  • sensors
  • soft sensors
  • autonomous greenhouse
  • mathematical models
  • resource optimization
  • robot
  • monitoring
  • predictive control
  • remote sensing
  • digital twin
  • production optimization

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse
Agriculture 2021, 11(8), 802; https://doi.org/10.3390/agriculture11080802 - 23 Aug 2021
Cited by 41 | Viewed by 1215
Abstract
Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes [...] Read more.
Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO2) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO2 concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses. Full article
(This article belongs to the Special Issue Future Development Trends of Intelligent Greenhouses)
Show Figures

Figure 1

Article
Prediction of Key Crop Growth Parameters in a Commercial Greenhouse Using CFD Simulation and Experimental Verification in a Pilot Study
Agriculture 2021, 11(7), 658; https://doi.org/10.3390/agriculture11070658 - 13 Jul 2021
Cited by 1 | Viewed by 857
Abstract
Controlled crop growth parameters, such as average air velocity, air temperature, and relative humidity (RH), inside the greenhouse are necessary prerequisites for commercial greenhouse operation. Frequent overshoots of such parameters are noticed in the Middle East. Traditional heating ventilation and air-conditioning (HVAC) systems [...] Read more.
Controlled crop growth parameters, such as average air velocity, air temperature, and relative humidity (RH), inside the greenhouse are necessary prerequisites for commercial greenhouse operation. Frequent overshoots of such parameters are noticed in the Middle East. Traditional heating ventilation and air-conditioning (HVAC) systems in such greenhouses use axial fans and evaporative cooling pads to control the temperature. Such systems fail to respond to the extreme heat load variations during the day. In this study, we present the design and implementation of a single span, commercial greenhouse using box type evaporative coolers (BTEC) as the backbone of the HVAC system. The HVAC system is run by a fully-automated real time feedback-based climate management system (CMS). A full-scale, steady state computational fluid dynamics (CFD) simulation of the greenhouse is carried out assuming peak summer outdoor conditions. A pilot study is conducted to experimentally monitor the environmental parameters in the greenhouse over a 20-h period. The recorded data confirm that the crop growth parameters lie within their required ranges, indicating a successful design and implementation phase of the commercial greenhouse on a pilot scale. Full article
(This article belongs to the Special Issue Future Development Trends of Intelligent Greenhouses)
Show Figures

Figure 1

Back to TopTop