A Study of the Effects of Enhanced Uniformity Control of Greenhouse Environment Variables on Crop Growth

In order to ensure high crop yield and good quality in greenhouse horticulture, the major environment control variables, such as temperature, humidity, and CO2 concentration, etc., need to be controlled properly, in order to reduce harmful effects on crop growth by minimizing the fluctuation of the thermal condition. Even though a hot water-based heating system is evidently superior to a hot air-based heating system, in terms of the thermally stable condition or energy saving, a hot air-based heating system has occupied the domestic market due to its economic efficiency from an initial investment cost saving. However, the intrinsic drawbacks of a hot air-based heating system, being more frequent variation of thermal variables and an inordinate disturbance on crops due to its convective heat delivery nature, are believed to be the main reasons for the insufficient crop yield and/or the quality deterioration. In addition, the current thermal environment monitoring system in a greenhouse, in which a sole sensor node usually covers a large part of cultivating area, seems to have a profound need of improvement in order to resolve those problems, in that the assumption of thermal uniform condition, which is adequate for a sole sensor node system, cannot be ensured in some cases. In this study, the qualitative concept of the new control variable—the degree of uniformity—is suggested as an indicator to seek ways of enhancing the crop yield and its quality based on the multiple sensor nodes system with a wireless sensor network. In contrast to a conventional monitoring system, for which a newly suggested concept of qualitative variable cannot be estimated at all, the multiple sensor nodes-based thermal monitoring system can provide more accurate and precise sensing, which enables the degree of uniformity to be checked in real-time and thus more precise control becomes possible as a consequence. From the analysis of the results of the experiment and simulation, it is found that the crops in plastic vinyl houses can be exposed to a serious level of non-uniform thermal condition. For instance, the temperature difference in the longitudinal and widthwise direction is 3.0 °C and 6.5 °C, respectively for the case of 75 × 8 m dimension greenhouse during a typical winter season, and it can be hypothesized that this level of non-uniformity might cause considerable damage to crop growth. In this paper, several variants of control systems, within the framework of the multiple sensor nodes system, is proposed to provide a more thermally-stable cultivating environment and the experimental verification is carried out for different scales of test greenhouses. The results showed that a simple change of heating mode (i.e., from a hot air- to a hot water-based heating system) can bring about a significant improvement for the non-uniformity of temperature (more or less 80%), and an additional countermeasure, with local heat flux control, can lead to a supplementary cut of non-uniformity up to 90%. Among the several variants of local heat flux control systems, the hydraulic proportional mass flow control valve system was proven to represent the best performance, and it can be hypothesized that the newly suggested qualitative variable—the degree of uniformity—with the multiple sensor nodes system can be a good alternative for seeking enhanced cultivating performance, being higher crop yield and better quality along with energy cost saving.

[1]  Lihong Xu,et al.  Energy Consumption Prediction of a Greenhouse and Optimization of Daily Average Temperature , 2018 .

[2]  Mahmoud Omid,et al.  Intelligent Control Based Fuzzy Logic for Automation of Greenhouse Irrigation System and Evaluation in Relation to Conventional Systems , 2009 .

[3]  D. Ahmad,et al.  Opportunities and Challenges for Climate-Smart Agriculture , 2015 .

[4]  Gao Jian,et al.  An Intelligent Greenhouse Control System , 2013 .

[5]  Ian Vázquez-Rowe,et al.  Assessing Energy and Environmental Efficiency of the Spanish Agri-Food System Using the LCA/DEA Methodology , 2018, Energies.

[6]  Erik D. Goodman,et al.  Greenhouse climate fuzzy adaptive control considering energy saving , 2017 .

[7]  Antonio Messineo,et al.  An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse , 2017 .

[8]  Emil Veg,et al.  Microclimate control in greenhouses , 2014 .

[9]  Viktoria Martin,et al.  Energy Analysis and Thermoeconomic Assessment of the Closed Greenhouse: The Largest Commercial Solar Building , 2011 .

[10]  Georgios K. Spanomitsios,et al.  SE—Structure and Environment: Temperature Control and Energy Conservation in a Plastic Greenhouse , 2001 .

[11]  K. Sumathy,et al.  Thermal modeling aspects of solar greenhouse microclimate control: A review on heating technologies , 2013 .

[12]  Doaa M. Atia,et al.  Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system , 2017 .

[13]  Eliseu Monteiro,et al.  Computational Fluid Dynamics Analysis of Greenhouses with Artificial Heat Tube , 2012 .

[14]  In-Bok Lee,et al.  Computation and field experiment validation of greenhouse energy load using building energy simulation model , 2015 .

[15]  Laurent Gauthier,et al.  A strategy for greenhouse climate control, part I: model development , 2001 .

[16]  J. Montero,et al.  ENVIRONMENTAL CONTROL AND CROP PRODUCTION IN MEDITERRANEAN GREENHOUSES , 2008 .

[17]  Belkacem Draoui,et al.  CFD simulation of heating greenhouse using a perforated polyethylene ducts , 2017 .

[18]  James W. Jones,et al.  Review of optimum temperature, humidity, and vapour pressure deficit for microclimate evaluation and control in greenhouse cultivation of tomato: a review , 2018 .

[19]  Pitam Chandra,et al.  Effect of greenhouse design parameters on conservation of energy for greenhouse environmental control , 2002 .

[20]  Francisco Rodríguez,et al.  Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control , 2009, Sensors.

[21]  Hyun Woo Lee,et al.  A Review of Greenhouse Energy Management by Using Building Energy Simulation , 2015 .