Temperature Modeling of a Greenhouse Environment

Temperature Modeling of a Greenhouse Environment

Sergio Steve Juárez-Gutiérrez (Universidad Politécnica de Aguascalientes, Mexico), Araceli Gárate-García (Universidad Politécnica de Aguascalientes, Mexico), Tania Aglaé Ramírez delReal (Universidad Politécnica de Aguascalientes, Mexico) and Ervin Jesús Alvarez-Sánchez (Universidad Veracruzana, Mexico)
DOI: 10.4018/978-1-4666-9779-9.ch013
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Abstract

This chapter presents the methodology to model the temperature inside a zenith greenhouse, without crop, using a Wireless Sensor Network (WSN) and the Recursive Least Square (RLS) Technique to estimate the modeling parameters. The greenhouse size is 6mx18m, the actuators are the motors located at the windows. The WSN collects the measurements and communicates the centralized control and the actuators located in different parts of the greenhouse, it is implemented using National Instruments devices, the graphical interface is developed in LabVIEW system design. There exist a lot of work about greenhouses in the literature; however, most of them did not use mathematical techniques to model the temperature or other environment parameter. Surprisingly, just few works use RLS techniques to estimate the parameters. The main contribution of this project is to integrate the National Instruments technology and the RLS techniques in a real experimental application.
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Introduction

Several reasons motivate the continuous interest and development in protected agriculture around the world, one of them is the impact of climate change, which affects the open-field agricultural activities, as well as the dynamics and competitiveness of marketing systems (Moreno et al, 2011). For several countries, the adoption of new technologies has allowed to increase production and availability of vegetables for population, at affordable prices during different year seasons, which is possible by means of controlling environmental requirements of the crops.

The common parameters to fulfill the ideal growth of the crop are temperature, relative humidity, soil moisture, ventilation, light and solar radiation, carbon dioxide, oxygen and fertigation, amongst others. It is possible to control them effectively in a greenhouse; however, a mathematical model is required to implement most of the control techniques (Leal et al, 2006).

The modeling and control of weather parameters inside a greenhouse has been studied in different parts of the world, such as: Almería, España (Díaz, 2002), Mexico (Leal et al, 2006), Israel (Seginer et al., 1993) and Japan (Morimoto & Hashimoto, 2000). An emphasis must be done in the studies of the Wageningen's University (Udink Ten Cate 1983, Tap, 2000), since Holland is the principal producer of Central Europe.

This topic has been studied since the 60’s with the work of (Businger, 1963), where it was suggested to use methods of energy balance to model the greenhouses behavior, however the existing models were useful in stable conditions of the climate, after that (Takakura et al, 1971) started to studied the subject using Fourier series. The work of (Udink Ten Cate, 1983) was one of the first to include a mathematical model considering the temperature inside the greenhouse, and it was improve by (Tchamitchian et al, 1992) considering the temperature changes in the greenhouse pipes.

There exist several literature about mathematical models for greenhouses based on different techniques, such as: (Gupta & Chandra, 2002, Lafont & Balmat, 2002, Ghosal & Tiwari, 2004, Luo et al, 2005, Salgado & Cunha, 2005, Abdel-Ghany & Kozai, 2006, Singh et al, 2006, Álvarez-Sánchez et al, 2014). Some works, like (Tap, 2000), consider several parameters to model the greenhouse conditions and have been the basis for other researches. Unfortunately, these models are generally obtained in an empirical form, and considering only one kind of greenhouse.

Moreover, several works in the last decade consider the automation of greenhouses, they are called intelligent, and they use neuronal networking, fuzzy control or similar techniques (see for instance, Laribi et al, 2006, Salazar et al, 2007, Ferreira & Ruano, 2008, Mohammad et al, 2010, Pengzhan & Baifen, 2010, Ma et al, 2010, Eredics & Dobrowiecki, 2011, Eredics et al, 2011). However, artificial networking has the inconvenient that the internal variables did not have necessarily a physical interpretation (Ferreira & Ruano, 2008).

Key Terms in this Chapter

Remote Monitoring System: Provides information of some system variables remotely.

Actuator: It is something, typically a mechanism, which converts energy to motion. The most common example is a motor, but it can be a pump, switch or valve.

Variable: It is the quantity or condition of the system that can be measured or can be manipulated to complain the task that is need.

Least Squares: Mathematical parameter estimation method that uses the response of a system to operate.

Natural Ventilation: Apertures in buildings that allow inflow of outside air.

Greenhouse: Structure that protects crops from sudden temperature changes.

System: Components that act together and realize certain function. From the control point of view is the process, machine, that will be manipulated to complain a task, it not necessary is physical, it can be a phenomena.

Vapor Pressure Deficit: Roughly speaking, it measures the reaction of the plant to the humidity in the growing environment, and it is an important factor that allows to control diseases and can be manipulated.

Mathematical Model: Mathematical representation of a system to describe the behavior of certain variables for an indeterminate time.

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