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Intelligent Greenhouses: Integrated Hierarchical

Management

Mehmet Ali DAYIOĞLU

Ankara University, Faculty of Agriculture, Department of Agricultural Machinery 06130, Aydinlikevler-Ankara, TURKEY

dayioglu@agri.ankara.edu.tr

Abstract: In this paper, a hierarchical control approach with three layers is defined to control the crop growth based on climate and economic criteria for tomato greenhouse. Integrated greenhouse management consists of energy, climate, water-fertilizer, crop quality and pest-disease management. The main purpose of a modern greenhouse is to define the information flow, to control optimum material and energy flow for crop production. The high-tech intelligent greenhouse uses expert software, communication links and many sensors. In other words, an intelligent greenhouse application includes information, control and hardware sub-systems. The hierarchical control architecture provides an integral solution among layers. The intelligent greenhouse technology targets lower energy, water, pesticide, and source use. High-tech greenhouses are managed according to the strategic, tactical and operational levels based on sensor, communication and software like an industrial plant. Key words: Intelligent greenhouse, integrated management, hierarchical management

INTRODUCTIONThe modern greenhouses are transparent

protective structures designed to produce vegetable, ornamentals and fruit crops under unsuitable environmental conditions all year-round. Mainly, high-tech greenhouse industry in the world is a successful sector of agriculture that increases the quality and quantity of products harvested (Boulard 2007). However, greenhouse production requires the use of large amounts of energy, water and agro-chemicals as well as waste disposal (Vox et al. 2010). A greenhouse with the high technology is mainly defined as a crop production factory.

In order to provide sustainable production throughout year, an efficient greenhouse management is necessary. Three basic functions are defined for a successful management: planning, implementation and control.

The planning function contains the decision making activities that lead to the construction of an acceptable decision, plan or strategy. The implementation function transforms a decision, plan or strategy into executable actions. The control function consists of monitoring and evaluating and revising the implementation of a decision, plan or strategy.

Greenhouse Management The hierarchical control architecture for

greenhouse can be derived from industrial automation system (Scattolini, 2009). The greenhouse hierarchical control architecture is shown in Fig. 1 adopted from Speetjensa et al., 2008. In order to define dynamics involving the greenhouse production process, three time scales are adequate as different from industrial plants. According to this architecture, the time scales such as months/season, hours/days, seconds/minutes are used for greenhouses.

The hierarchical management concept consists of seven layers as follows (Speetjensa et al., 2008): Layer 1: Concerns primary technology and

hardware including window motor, fan motor, water pumps, motor valves, magnetic valves.

Layer 2: Consists of sensor (temperature, humidity, PAR, CO2) and actuators (motor, solenoid).

Layer 3: Contains the control of individual actuators, low level control operations (fan speed control, pump speed control, window position control).

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Figure 1. Greenhouse management pyramid (adopted from Speetjensa et al., 2008)

Layer 4: Consists of control operations defined as

group control such as heating system, cooling system, fogging system.

Layer 5: Presents to operator the information about the whole system, complex operations such as optimization, helps to make expert system decisions and maintenance and requires a knowledge database.

Layer 6: creates summary information about automation, daily information and higher level data.

Layer 7: provides planning, processing and decision making for longer time frames including month and season. The greenhouse production contains fast crop

dynamics and slow crop development. The fast dynamics are temperature, humidity, transpiration, photosynthesis, and respiration. The slow dynamics are crop growth and fruit changes. Therefore, a multilayer hierarchical control architecture can be proposed for greenhouse management (Rodriguez et al., 2008) Multilevel hierarchical control architecture

In the greenhouse agro-system, 1-5 layers depicted in Fig. 1 can be combined as a single layer apart from industrial automation because these layers has the same time scales.

Consequently, the greenhouse agro-system can be

managed by using a hierarchical control architecture where system is divided into three different layers as shown in Fig. 2 (Ramirez-Arias et al., 2012). First layer: Climate control and nutrition control

The first layer includes the controllers that compute adequate control signals for actuators considering the set-points (for example temperature and EC set-points) calculated by the upper layer. The control algorithms including feed-forward control, adaptive control, predictive control and hybrid control have been proposed for greenhouse control (Rodriguez et al., 2008).

When a night–day transition occurs, the optimization problem is again solved using new real measured data of climatic variables and crop growth, trying to reduce errors coming from plant-model mismatch, deviations in the weather forecast, or even because the climatic variables are not able to reach the set points. Second Layer: Set-point adaptation

The set-point values to be sent to the lower layer for the next day are modified and updated to prevent error problems. These modifications are performed by considering the trajectories generated in the upper layer, the short-term weather prediction, the current state of the crop, the short-term grower goals. Using

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Figure 2. Multilevel hierarchical control architecture (adapted from Ramírez-Arias et al. 2012)

this information in models, the greenhouse behavior is simulated and evaluated for setpoints defined Rodríguez et al., 2008). The optimization procedure is repeated by decrease or increase of the setpoints according to the simulation results. Setpoints are generated by mathematical models in software tools and send to the climate computer that controls the actuators (Aaslyng et al., 2005; Speedjens et al. 2008). The new setpoints are sent to the lower layer. Third Layer: Crop growth control layer

This layer solves an optimization problem as a function of the expected production and associated costs or the desired date of harvesting. This optimization problem maximizes an objective function that represents the profit obtained based on the climatic variables, providing as results the set-points that these climatic variables must follow along the season (Rodríguez et al., 2008).

The problem solution of crop growth control based on the greenhouse climatic conditions and considering economic criteria to reach the desired objective. The optimization process obtains the optimal set-point trajectories of the air temperature, which is the main control variable that affects the crop growth, two temperature set-points per day are considered (one for diurnal time and another for nocturnal time), and the system is well irrigated and fertilized (Ramírez-Arias et al., 2012).

Optimization

The crop growth can be defined by two dynamic models represented with differential equations (Ramírez-Arias et al., 2012):

)t,V,D,U,X,X(fdt

dXcgc

c (1)

)t,V,D,U,X,X(fdt

dXcgg

g (2)

Where Xc a vector of greenhouse climate state variables including inside air temperature,

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humidity,CO2 concentration, PAR, soil surface temperature, cover temperature and leaf temperature. Xg a vector of crop growth state variables including number of nodes on the main stem, leaf area index (LAI), total dry matter (root, stem, leaves, flower and fruit) excluding water, fruit dry matter as biomass excluding water, and mature fruit dry matter or mature fruit biomass accumulation. U is vector of input variables about natural ventilation, heating system, fogging system. D is vector of disturbances about outside temperature, humidity, wind speed and direction, solar radiation, and rain. V is vector of system variables related to transpiration, condensation, photosynthesis and other processes. C is vector of system constants, t is the time, fc is a nonlinear function based on heat and mass balances, fg is nonlinear function based on physiological processes of plants.

Linear and non-linear models have been developed using physical laws (van Henten 1996; Bennis et al., 2008; Ehret et al., 2011). These models contain climate model and growth model equations, different objective functions, decision variables of optimization problem (van Straten et al., 2000; Aaslyng et al., 2005; Bram et al., 2012)

The target functions of optimization procedures can be expressed by using maximization of profits, maximization of quality, and maximization of water use efficiency defined with (3), (4) and (5) equations as follows, respectively:

dt)t(Vcdt)t(X)t(VJth

tiP

th

ti

p1 (3)

Where Vp(t) is the selling price of the production estimated from the market. Xp(t) is the fresh fruit production obtained from the crop growth model. Vc(t) are the costs of heating, electricity, fertilizer and water, t is time, ti is the initial time of crop cycle and th is the latest harvesting time. In the practice, the tomato crop has multiple harvest during the growing season; this state is considered for each harvest. For example, the fresh fruit production (Xp(t)) and the process costs (Vc(t)) can be estimated for process decision variables (p) that are Xta for inside air temperature and XEC for electrical conductivity.

dt))t(Q)t(Q)t(Q)t(Q( 432

th

ti12J

(4)

Where Q1 – Q4 are parameters defined for fruit quality. In tomato fruits, soluble solids, titratable acidity, fruit firmness and fruit size are important for fruit quality.

In arid and semi-arid climates, water is very scarce and an expensive resource that is necessary during hot seasons of the year. Water-use efficiency is considered for the biomass efficiency according to the relation between the fresh fruit and the water supplied. The supplied water amount depends on multiple factors including PAR, pH, EC, air temperature, and is defined with below function:

dt)t(W

)t(XJ

th

ti s

p

13

(5)

where Xp is the fresh fruit production and Ws is the supplied water. Integrated Greenhouse Management

The sophisticated greenhouse production techniques rely on the use of computer technologies both for monitoring and for precise control of the processes involved in the production cycle of high quality plant products (Aaslyng et al., 2005). Knowing the status of the plant-greenhouse system in real time, requires the use of sensors and controllers for monitoring and controlling each of the environmental variables. Due to need of environmental control responses to the continually changing outdoor climate, and the potential failure of the systems within the controlled environment system, it is valuable to have an information system capable of off-site monitoring of the greenhouse environment. Internet technologies offer a possible means of communication (Morimoto et al, 1996; Rodrigez et al., 2008).

Modern greenhouses must provide following specifications: Increasing the production, Decreasing the product cost, Improving the product quality, Flexible application for short and long targets Effective using of energy, Using of alternative energy sources, industrial

waste heat

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Decreasing of greenhouse gas emission Decreasing of environmental pollutions, wastes, Decreasing of pesticide use

Modern greenhouses can be activated with the integrated management concept. In order to come up with to targets above following, the integrated system management can be employed (Boulard, 2007).

Integrated greenhouse management shown in Fig.3 includes energy management (Kolokotsa, 2010), climate management (van Henten 1996), water-fertilizer management (Pardossi et al., 2011), crop management (De Pascale and Leonardi, 2012), quality and pest-disease management (Albajes et al. 2000).

According to market dynamics that include final product, fertilizer and energy prices, greenhouses are can be strategically managed. (Kolokotsa, 2010). Greenhouse manager takes for strategic decisions of effective and flexible greenhouse management. Integrated greenhouse management concept uses information and automation technologies in all layers. The main purpose of a modern greenhouse is to define the information flow, to control optimum material and energy flow for crop production.

Components of an intelligent greenhouse are schematically shown in Fig. 4. The high-tech greenhouse require expert software, communication links and many sensors for efficient automation and optimum crop growth (Fortuna et al., 2000). For this purpose, a central management unit and a greenhouse computer are used. Greenhouse computer monitor all data about climate, fertigation, pest-disease and crop status as well as alarm information. The information flow in the greenhouse can be provided with telemetry and sensor system, wired and/or wireless communication units. The information technologies such as Ethernet, RS485, CAN, ModBus can be used for long-range data transfer and automation (Serodio et al. 2001). Wireless technologies including Bluetooth, WiFi, ZigBee can be employed for short range solutions as well as GSM for long range (Pawlowski et al., 2009).

An intelligent greenhouse can be defined as shown in Fig. 5 based on the integrated system. The smart greenhouse technology provides to be managed using sensor, communication and software substructure of greenhouse (Aaslyng et al., 2005).

High-tech greenhouses are managed according to the strategic, tactical and operational levels based on sensor, communication and software like an industrial plant.

Figure 3. Integrated greenhouse management

concept Some inputs such as energy use, crop growing

and quality, and water-fertilizer use of greenhouse are continuously succeeded, and recorded along with climate regime Climate and pest and disease regimes target the minimum pesticide use. Alternative solutions such as geothermal, solar energy and waste heat can be used for greenhouse heating as well as active-passive thermal storage. Furthermore, quality water requirement can be provided with rainwater harvest and storage. CONCLUSIONS

The greenhouse agro-system is a complex system including physical, chemical and biological processes in which are take place simultaneously. Therefore, the crop growing conditions can be just modified by sub-systems including heating and screening system, ventilating system, cooling and shading system, misting and fogging system, CO2 injection system irrigation and fertigation system, sensors and automation system, transportation and packaging.

Sophisticated greenhouse production techniques rely on the use of computer technologies both for monitoring and for precise control of the processes

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involved in the production cycle of high quality plant products. Knowing the status of the plant-greenhouse system in real time, requires the use of sensors and

controllers for monitoring and controlling each of the environmental variables.

Figure 4. Information and automation technology for intelligent greenhouses

Figure 5. Intelligent greenhouse technology

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Integrated greenhouse management consists of components such as energy management, climate management, water-fertilizer management, crop quality and pest-disease management.

The main purpose of a modern greenhouse that is industrial facility is to define the information flow, to control optimum material and energy flow for crop production. Information technology consists of expert software, communication links and many sensors. A smart greenhouse application includes information, control and hardware sub-systems. The smart greenhouse technology provides effectively to be managed using sensor, communication and software substructure of greenhouse. Greenhouse computer monitor all data about climate, fertigation, pest-disease and crop status as well as alarm information.

Greenhouse control strategies have been combined according to the hierarchical control architecture implemented in a high-tech greenhouse.

Today, innovations in greenhouse technology have put into practice coupled with industrial development. They occur according to the operational demands on the system and to strategic changes about production. High-tech greenhouses may provide maximum productivity and fruit quality by means of optimal plant growth environmental controls year-round. REFERENCES Aaslyng, J. M., N. Ehler, L. Jakobsen, 2005. Climate control

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