Volume 9, Number 2 June 2017 - Agricultural Science and Technology

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ISSN 1313 - 8820 (print) ISSN 1314 - 412X (online) Volume 9, Number 2 June 2017

2017

Editor-in-Chief

Tsanko Yablanski (Bulgaria) Atanas Atanasov (Bulgaria) Svetlana Georgieva (Bulgaria) Nikolay Tsenov (Bulgaria) Max Rothschild (USA) Ihsan Soysal (Turkey) Horia Grosu (Romania) Stoicho Metodiev (Bulgaria) Bojin Bojinov (Bulgaria)

Scope and policy of the journal Agricultural Science and Technology /AST/ – an International Scientific Journal of Agricultural and Technology Sciences is published in English in one volume of 4 issues per year, as a printed journal and in electronic form. The policy of the journal is to publish original papers, reviews and short communications covering the aspects of agriculture related with life sciences and modern technologies. It will offer opportunities to address the global needs relating to food and environment, health, exploit the technology to provide innovative products and sustainable development. Papers will be considered in aspects of both fundamental and applied science in the areas of Genetics and Breeding, Nutrition and Physiology, Production Systems, Agriculture and Environment and Product Quality and Safety. Other categories closely related to the above topics could be considered by the editors. The detailed information of the journal is available at the website. Proceedings of scientific meetings and conference reports will be considered for special issues.

Nutrition and Physiology

Submission of Manuscripts

Georgi Petkov Faculty of Agriculture Trakia University, Stara Zagora Bulgaria E-mail: [email protected] Co-Editor-in-Chief Dimitar Panayotov Faculty of Agriculture Trakia University, Stara Zagora Bulgaria Editors and Sections Genetics and Breeding

Nikolai Todorov (Bulgaria) Peter Surai (UK) Ivan Varlyakov (Bulgaria) George Zervas (Greece) Vasil Pirgozliev (UK) Production Systems Radoslav Slavov (Bulgaria) Dimitar Pavlov (Bulgaria) Bogdan Szostak (Poland) Banko Banev (Bulgaria) Georgy Zhelyazkov (Bulgaria) Agriculture and Environment Martin Banov (Bulgaria) Peter Cornish (Australia) Vladislav Popov (Bulgaria) Tarek Moussa (Egypt) Product Quality and Safety Stefan Denev (Bulgaria) Vasil Atanasov (Bulgaria) Roumiana Tsenkova (Japan) English Editor Yanka Ivanova (Bulgaria)

There are no submission / handling / publication charges. All manuscripts written in English should be submitted as MS-Word file attachments via e-mail to [email protected]. Manuscripts must be prepared strictly in accordance with the detailed instructions for authors at the website www.agriscitech.eu and the instructions on the last page of the journal. For each manuscript the signatures of all authors are needed confirming their consent to publish it and to nominate on author for correspondence. They have to be presented by a submission letter signed by all authors. The form of the submission letter is available upon from request from the Technical Assistance or could be downloaded from the website of the journal. Manuscripts submitted to this journal are considered if they have submitted only to it, they have not been published already, nor are they under consideration for publication in press elsewhere. All manuscripts are subject to

editorial review and the editors reserve the right to improve style and return the paper for rewriting to the authors, if necessary. The editorial board reserves rights to reject manuscripts based on priorities and space availability in the journal. The journal is committed to respect high standards of ethics in the editing and reviewing process and malpractice statement. Commitments of authors related to authorship are also very important for a high standard of ethics and publishing. We follow closely the Committee on Publication Ethics (COPE), http://publicationethics.org/resources/guid elines The articles appearing in this journal are indexed and abstracted in: DOI, EBSCO Publishing Inc. and AGRIS (FAO). The journal is accepted to be indexed with the support of a project № BG051PO0013.3.05-0001 “Science and business” financed by Operational Programme “Human Resources Development” of EU. The title has been suggested to be included in SCOPUS (Elsevier) and Electronic Journals Submission Form (Thomson Reuters). The journal is freely available without charge to the user or his/her institution. Users can read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This issue is printed with the financial support by Contract No DNP 0521/20.12.2016, financed from Fund 'Scientific Research' grant Bulgarian scientific Periodicals. Address of Editorial office: Agricultural Science and Technology Faculty of Agriculture, Trakia University Student's campus, 6000 Stara Zagora Bulgaria Telephone: +359 42 699330 +359 42 699446 www.agriscitech.eu Technical Assistance: Nely Tsvetanova Telephone: +359 42 699446 E-mail: [email protected]

Volume 9, Number 2 June 2017

ISSN 1313 - 8820 (print) ISSN 1314 - 412X (online)

2017

AGRICULTURAL SCIENCE AND TECHNOLOGY, VOL. 9, No 2, pp 132 - 139, 2017 DOI: 10.15547/ast.2017.02.024

Agriculture and Environment

Modeling and simulation of fuzzy logic controller for optimization of the greenhouse microclimate management Didi Faouzi*1, N. Bibi-Triki1, B. Draoui2, A. Abene3 1

Materials and Renewable Energy Research Unit M.R.E.R.U, University of Abou-bakr Belkaïd, B.P. 119Tlemcen, Algeria Energy Laboratory in Drylands, University of Bechar, Bechar Algeria 3 Euro-Mediterranean Institute of Environment and Renewable Energies, University of Valenciennes, France 2

(Manuscript received 21 November 2016; accepted for publication 8 June 2017) Abstract. Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and the factors are light, temperature and relative humidity. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year. To effectively meet this demand, greenhousesystems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc.). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc.) New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence such as neural networks and / or fuzzy logic, etc. As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year, The application of artificial intelligence in the industry known for considerable growth, which is not the case in the field of agricultural greenhouses, where enforcement remains timid. It is from this fact, we undertake research work in this area and conduct a simulation based on meteorological data through MATLAB Simulink to finally analyze the thermal behavior - greenhouse microclimate energy.

Keywords: agriculture greenhouse, microclimate, modeling, fuzzy logic controller, optimization, solar energy, climate model

Introduction In recent years, the cultivation of horticultural plants under greenhouses has increasing application in Algeria. The reason for this development is the standard of living of the population, on the one hand, and the increased demand for fresh produce throughout the year. The use of greenhouses is an important technique in the development of agriculture; however, a decrease in production is noticed during the coldest, due to a significant loss of energy through the walls of the greenhouses. This inadequate maintenance of an adequate climate in the greenhouse has production of cultivated products, consequently a change in its sale would be justified (Gurbaoui and al., 2013). Greenhouse cultivation has been undergoing significant development for several years to face an increasingly competitive market and conditioned by quality standards severe(Bendimerad et al., 2014). Production greenhouse systems are becoming considerably sophisticated and therefore disproportionately expensive. For this reason, locksmiths who want to remain competitive must optimize their investment by a greater * e-mail: [email protected]

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control of the conditions of production (Bibi-Trikiet al., 2011). For fully exploit the increased opportunities for crops, it is essential to adjust the state of the system in an automatic manner. Better management of climatic parameters under glass today constitutes a major challenge at the global level (El Aoud and Maher, 2014). This is particularly true in the field of agriculture. Greenhouse systems must be modernization and move more towards the use of new cropping techniques and the appropriate automatic devices. To improve profitability, there is a need to increase cultures in optimal environments. It is therefore important to control the temperature air, moisture, and CO2 content (Didi Faouzi et al., 2016). All these factors must be considered in the energy balance, because each of them can influence others. During the last years, greenhouse equipment manufacturers have met the expectations of new technologies and new products that level of efficiency in greenhouse production (Hasni et al., 2009). Advances in computing, including increased capabilities of computers, have considerably contributed to the automatic control of processes climate change (Geethanjali et al., 2007). Computer-assisted

management has become an indispensable tool in the study of the climate in greenhouses. It contributes to improving the climatic conditions cultivation under glass. But above all, it is the only rational way to predict climate change under glass. The improvement of article work on the structure of the greenhouse, on the choice of coverage, on the choice of the site and the bioclimatic stage. The growth of the sheltered plant depends on the temperature of the indoor air of the greenhouse kept in the shelter. Among the means of climate control during the summer period (Yih-Lon Lin et al., 2007), the greenhouses plays a key role in reducing the temperature (Bouaama et al., 2008; Dhamakale and Patil, 2011). The heating of greenhouses in winter is also important because it allows increasing the temperature inside the greenhouse (Abdelhafid Hasni et al., 2008). The thermal performance of the greenhouse must also be taken into account, especially with regard to their insulation, thermal storage and heating equipment (Draoui and al., 2013). The present study is part of a contribution to the development of greenhouse farming and aims to develop intelligent controllers (Chau and al.,2006) to optimize the management of parameters under greenhouse conditions (Liao and al., 2007). For this reason we have done the modeling of a fuzzy controller by the application of fuzzy logic using Mamdani method.

Material and methods 1. Modeling the greenhouse This article deals with the modeling and simulation of our greenhouse model which is based on the method of GUESS (Jamisson, 2006). GUESS is a model set in parameter block, meaning that spatial heterogeneity is ignored and it is assumed that the inner content and the flow through the system boundary are evenly distributed. The conservation equations are used to model the rate of system status change. џ For a warm greenhouse these state variables would be the indoor temperature, relative humidity, air pressure and CO2 concentration. џ For the plant state variables are the water content, the body temperature, dry weight or biomass, and internally sheet CO2. A complete equation for the transport of some scalar quantity through a control volume is as following:

The effectiveness of the typical tablet is about 85%. The heat loss rate depends on the fan speed. (2) (3) (4) where: η pad is pad efficiency Tout, Twb are the difference between the outside temperature and wet bulb (K); Cp is specific heat (J/kg.k ); p is density (kg /m3); V is fan speed (m/s). 2.2. Model of fogging systems The flow of steam and heat are determined through Ohm's Law and is as following: (5) (6) Where: q is the heat transfer between the nebulizer and the air of agricultural greenhouse (W/m2); K is global coefficient of heat transmission (W/m2.k); Psat is saturation pressure (Pascal); Pair is Ambient air pressure (Pascal); λ is thermal conductivity (W/m2.k). 2.3. Evaluation Model of the wall temperature (TP) The TP wall temperature evaluation model (Bibi-Triki et al., 2011), closest to reality is determined based on the average temperatures TPi and TPe: (7) The indoor and outdoor temperatures TPi and Tpe are: (8)

The temperature evaluation model of Tp wall will be expressed:

(1) Where: C is the heat capacity (J/m3.k), V is system volume (m3); Φ is a quantity describing the state of the system (W / m2);dx is material thickness (m); A is the flow boundary surface (control surface) (m2); Fint, Fout areinternal and external flux (W/m2). 2. Modeling the climate of the greenhouse systems 2.1. Cooling pads Model In a greenhouse, evaporative cooling devices are used to reduce the temperature when the fan cannot reach appropriate levels for optimal plant growth. In equipped greenhouses, cooling evaporation is the second part of the unrealized gain. Most evaporative cooling methods can be modeled as adiabatic cooling process; the minimum temperature and the achievable maximum vapor pressure is equal to the wet bulb (Jamisson, 2006).

Where: Tair, I,Tair, e is dry air temperature inside/outside (K); hPi and hPe is coefficient of superficial exchanges at the inter wall, of the outer wall (W/m2.k); CB is quotient de Bibi. This report dimensionless CB is used in evaluating the Tp wall temperature, it is now called the quotient of Bibi, it is the ratio of the difference of surface thermal exchange by conduction, convection and radiation occurring at the level of the greenhouse coverage. 2.4. Heating systems The heat produced per unit of fuel is modeled as (Jamisson, 133

2006):

is the temperature of the exhaust gas (k); r is the return ratio. 3. Energy balance of the greenhouse (9)

Where: hcombution sensible is sensible heat load of a condensing water heater (J); LHV is lower heating value (KJ/kg); Φ is the fuel air; 36/16 is the weight ratio of the produced steam to supply the burner; Texhaust

The analytical energy balance equation of the greenhouse: Stored energy change = (Gain from internal sources+ Gain from the sun)–(Losses due to conduction through the cover + Losses due to long wave radiation + Unrealized losses /evaporation/+ Losses due to the exchange of air).

(10) Where esat indicates the report saturated with the relative humidity in the sub-model of combustion (Kg steam / kg air); is the heat provided by the heating system (W); rconv, in rconv, out are heat transfer coefficients - inside and outside by convection (W/m2.k).

air); ζ is the number of moles of carbon per mole of fuel; Vinf is ventilation rate (m3 air/s); Fphotosyntesis is the amount of heat supplied by photosynthesis (W).

4. The mass transfer in the greenhouse The mass balance for moisture in the greenhouse can be written as following (eq. 11):

Photosynthesis is a complex process. CO2 fixation and subsequent conversion into carbohydrates are not a single reaction, but a series of steps, the Calvin cycle (Figure 1).

5. Photosynthesis

(11) where: Vinf is the speed of air infiltration (m/s); V greenhouse is the total volume of agricultural greenhouse (m3); Hin, Hout is the indoor and outdoor humidity (KJ/kg); Vvent is ventilation rate (m3 air/s). And for the humidity balance: Rates of change in absolute humidity = Infiltration + Ventilation * (humidity difference with the outside) + Misting + Cooling + AND Condensation The status of humidity function is (eq. 12): Figure 1. Schematic Calvin cycle

where: CO2 is mass balance in molar units (ppm or μmol CO2 per mol 134

The reaction at the apex (CO2 fixation and RuBP) is catalyzed by the enzyme Rubisco. This reaction ordered carbon assimilation rates, and that is modeled by Farquhar and al. (2006) equations. Source: Cellupedia, “Calvin cycle” (2002). According to Farquhar model, the CO2 compensation model is:

(14) Farquhar model with Γ, CO2 compensation point; Ci Internal CO2 concentration (ppm). 6. Plant state of water balance The equation of thermal balance of water is as follows: (15) where Ψ is the potential of water; Cplant is the capacity of the plant (mole*m2); E is evapotranspiration; Aroot is the root surface (m2); Rroot is the growth rate. In the model of GUESS, we assume that the soil is well watered, so that the physiological effects of the state of water should be minimal, except in stomata. 7. Stomatal conductance and balance CO2

variable to defuzzification. It is possible, and in some cases much more efficient to use a single peak as output membership function, rather than a distributed fuzzy set. This is sometimes known as singleton output membership function, and we can think like a fuzzy set of pre defuzzification. It improves the efficiency of defuzzification because it greatly simplifies the calculation required by the more general method Mamdani which has the center of gravity of a twodimensional function. To calculate the output of the SIF in view of inputs, six steps should be followed: џ The determination of a set of fuzzy rules. џ Fuzzification inputs using the input membership functions. џ By combining Fuzzificaion entries according to the fuzzy rules to establish a resistance to the rule. џ Find the consequence of rule by combining the resistance to the rule and the output membership function. џ By combining the consequences to get a distribution outlet. џ Defuzzification the output distribution. 10. Fuzzy sets

The rate of photosynthesis in the Farquhar model depends on the internal concentration of CO2. To determine the concentration of CO2, a mass balance is performed on the sheet. (16) According to GUESS the plant stomatal equation of is:

(17) Ball-Berry modified model used in GUESS, where: gstomatal is stomatal conductance in units of (mole.s-1.m-2.). Plant energy balances. Equation for the temperature of a Leaf is as follow:

(18) 8. Fuzzy logic controller modeling Fuzzy logic is widely used in the machine control. The term "fuzzy" refers to the fact that the logic can deal with concepts that cannot be expressed as the "true" or "false" but rather as "partially true"[9]. While alternative approaches such as genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution can be cast in terms that human operators can understand, so that their experience can be used in the design of the control device. This makes it easier to mechanize the tasks have already been performed successfully by man. 9. Fuzzy inference method Mamdani Fuzzy inference Mamdani type, as defined for Toolbox fuzzy logic, expects the output membership functions to be fuzzy sets. After the aggregation process, there is a fuzzy set for each output

The input variables in a fuzzy control system are generally mapped by sets of membership functions similar to it, called "fuzzy set". The process of converting a crisp input value to a fuzzy value is called "fuzzy logic". A control system may also have different types of switch, or "ON-OFF", inputs and analog inputs and during switching inputs will always be a truth value of 1 or 0, but the system can handle as simplified fuzzy functions happen to be one value or another. Given "mappings" of input variables membership functions (A) (Figure 2) and truth values, the microcontroller then makes decisions for action on the basis of a set of "rules” (B). B. Rules of decisions џ If (Ti is TVCOLD) then (FOG1FAN1 is OFF)(FOG2FAN2 is OFF)(FOG3FAN3 is OFF)(NV is OFF)(Heater1 is ON)(Heater2 is ON)(Heater3 is ON) (1) џ If (Ti is TCOLD) then (FOG1FAN1 is OFF)(FOG2FAN2 is OFF)(FOG3FAN3 is OFF)(NV is OFF)(Heater1 is ON)(Heater2 is ON)(Heater3 is OFF) (1) џ If (Ti is TCOOL) then (FOG1FAN1 is OFF)(FOG2FAN2 is OFF)(FOG3FAN3 is OFF)(NV is OFF)(Heater1 is ON)(Heater2 is OFF)(Heater3 is OFF) (1) џ If (Ti is TSH) then (FOG1FAN1 is OFF)(FOG2FAN2 is OFF)(FOG3FAN3 is OFF)(NV is ON)(Heater1 is OFF)(Heater2 is OFF)(Heater3 is OFF) (1) џ If (Ti is TH) then (FOG1FAN1 is ON)(FOG2FAN2 is OFF)(FOG3FAN3 is OFF)(NV is OFF)(Heater1 is OFF)(Heater2 is OFF)(Heater3 is OFF) (1) џ If (Ti is TVH) then (FOG1FAN1 is ON)(FOG2FAN2 is ON)(FOG3FAN3 is OFF)(NV is OFF)(Heater1 is OFF)(Heater2 is OFF)(Heater3 is OFF) (1) џ If (Ti is TEH) then (FOG1FAN1 is ON)(FOG2FAN2 is ON)(FOG3FAN3 is ON)(NV is OFF)(Heater1 is OFF)(Heater2 is OFF)(Heater3 is OFF) (1) 11. Simulation and model validation Our model is based on the greenhouse GUESS model that is set for a multi greenhouse chapel which each module is 8.5 m wide, 34 m deep and ridge height of 4.5 m. Infiltration rate is 1.1 air changes per hour, and a U value of 5.76 W/m2.K was used. The model of the plant was set for Douglas seedling plants were started at 0.57 g dry weight, and harvested 1.67 g dry weight; a new growing 135

season was recorded at harvest. A set of hourly data for 2015 (1 January to 31 December) weather station of Dar El Beida, Algeria was used to validate our model as a CSV file that consists of four columns (global solar radiation, temperature, humidity and wind speed). The model of the greenhouse was coded using the full version of Windows MATLAB R2012b (8.0.0.783), 64bit (win64) with

Simulink. The simulation was performed on a Toshiba laptop. The laptop is equipped with a hard drive 700 GB and 5 GB of RAM. Simulink model of the parties were made in "Accelerator" mode that has first generated a compact representation of Code C of the diagram, then compiled and executed. Greenhouse global Model and Fuzzy logic controller simulation model of the greenhouse are shown in Figures 3 and 4.

Figure 2. Representation rules of membership

Figure 3. Simulink representation of the global greenhouse climate model

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Figure 4. Simulink representation of the fuzzy logic controller model

Indoor vs. Outdoor Climatic Conditions

Results and discussion Temp. (C)

30 20 10

Relative Humidity % of 100

0 120 100 80 60 40 20 0

0

50

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150

200 Day

250

300

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400

0

50

100

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200 Day

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Figure 5. The evolution of humidity and temperature (interior / exterior)

x.10-4 7

Indoor Temperature Distribution

6 5 Frequency

The simulation results clearly visualize the actual thermoenergy behavior of agricultural greenhouse, applying the model of artificial intelligence, namely the application of fuzzy logic (Figure 5). We added to our model of the greenhouse an intelligent regulator using the fuzzy logic and we chose the Mamdani method with a single input, we started by first defining the input data and the outputs, and by the following has been attempted to link the membership functions in a logical manner in order to respond to the following steps: Then the range of variations (the fuzzy sets) and the membership functions for the input and the output were defined and each part of the membership function was called by a significant name (Figure 2). After defining the membership functions, the inference rules have been implemented in such a way as to achieve optimum control as desired, for example if the climate inside the greenhouse becomes lime the regulator will automatically Lower the temperature by closing a heating system or opening a cooling system or by any other means and all this, in order to keep the required instruction which will be translated by the command (Rules of decisions). We save the file (.fis) to load it into the workspace and retrieve it in the Simulink Fuzzy block under the same name of the saved file. The simulation of our system was done by MATLAB / SIMULINK. The results of the MATLAB / SIMULINK software indicate the high capacity of the proposed technique to control the internal temperature of the greenhouse even in the event of a rapid change in atmospheric conditions. The modeling of the system is defined in the form of this block diagram introduced in our Simulink shown in (Figure 5). Its goal is to achieve the set temperature of 20°C required by the internal environment of our greenhouse. Indeed, by varying the ranges of inferences, the efficiency of the regulator has been increased around this set point. It would also be possible to modify the inference rules or the forms of the membership functions used. It is found that most internal temperature values are between (15°C and 25°C) in Figure 6 for the winter period and between (20°C and 28°C) for the spring summer period. Temperature variation during the autumn and winter period is due to heat loss overnight and

40

4 3 2 1 0 10

12

14

16

18 20 22 24 Temperature, °C

26

30

28

Figure 6. Histogram shows the distribution of indoor temperature

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the noise appearing in the room temperature signal is caused by the cyclic activation and deactivation of the heating systems And ventilation. However, the compensation of the heating is insufficient and expensive, for this reason an improved thermal insulation for the wall of the cover is necessary. The improved thermal insulation of the lid can be practically achieved by the addition of a layer of plastic air bubble mounted on the inner wall face. During the spring summer period the temperature is almost in the desired range in both regions except for the half of the summer where the temperature is a little increase. The use of cooling and spraying systems is necessary to lower the

temperature in the desired range. But this solution is insufficient and really costly, for this purpose we should improve the characteristics of the cover of the agricultural greenhouse, double wall thermal insulation that demonstrates improved heating and cooling efficiency, etc. The relative humidity in Figure 7 is generally close to the optimum for the entire year except summer when humidity falls below the threshold due to the significant vaporization used for temperature compensation. This problem of adding a screen on the roof of the greenhouse for the region of Dar El Beida.

Figure 7. Plant growth characteristics (Figure 7-1. Plant height, cm; Figure 7-2. Plantrod diameter, mm; Figure 7-3. Plant total biomass (dry weight); Figure 7-4. Crops harvested)

The speed of growth of the mass of the plant is normal for most of the year except in the end of autumn and beginning of winter because of the temperature drops at night and we discussed this problem and its correction previously. The figure 7 represents the characteristics of the plant and how often the product is harvested for one year in each season, also the speed of the harvest in each period, such that it can be seen that by application of our blurred controller we can harvest four times In a year but at the end of the year to December and beginning of the year to January it is observed that the operation of the harvest or the growth of the plant is a little heavy and take little more time than the other seasons. This negative growth is caused by insufficient integral light during the winter and which means a break in our model and a lowering in light and photoperiod. And this indicates that despite the results obtained in the desired interval, our blurred controller found 138

difficulties for the optimal climate management inside our model of the greenhouse and exactly in the winter period (end of December and beginning of January) and this indicates that our system is unstable in this period. And that makes us think of its development and improve it in the future to get better results.

Conclusion However, our objective is achieved to the extent that it has been shown through modeling and control by the use of fuzzy logic, this area is very difficult because it is a multi-control variable which the greenhouse is a biophysical system where parameters are highly correlated as shown by the results. This technique of fuzzy logic that has been adapted to the greenhouse to a promising future for the climate control and management of the greenhouse for greenhouse

growers, it is a preferred approach for structuring and knowledge aggregation and as a means of identification of gaps in the understanding of mechanisms and interactions that occur in the system - greenhouse. Fuzzy logic is a branch of artificial intelligence, which must point out its advantages and disadvantages. Its use has led to quite satisfactory results of the control and regulation perspective. We remain optimistic in the near future, as to the operation of artificial intelligence, including the use of fuzzy logic which indicates: џ For the control and regulation of the greenhouse microclimate. џ By the conservation of energy. џ For the efficiency of energy use in the greenhouses operation. џ For improved productivity of crops under greenhouses. џ In a significant reduction of human intervention.

References Abdelhafid Hasni B, DraouiT, Boulard R Taibi and Hezzab A, 2008. Evolutionary algorithms in the optimization of greenhouse climate model parameters. International Review on Computers and Software. Bendimerad S, Mahdjoub T, Bibi-Triki N, Bessenouci MZ and Draoui B et al., 2014. Simulation and Interpretation of the BIBI Ratio CB, as a Function of Thermal Parameters of the Low Inertia Polyethylene Wall of Greenhouses. Physics Procedia, 55: 157-164. DOI: 10.1016/j.phpro.2014.07.023 Bibi-Triki N, Bendimemerad S, Chermitti A, Mahdjoub T and Draoui B, 2011. Modeling, characterization and analysis of the dynamic behavior of heat transfers through polyethylene and glass walls of greenhouses. Physics Procedia, 21: 67-74. DOI: 10.1016/j.phpro.2011.10.011 Bouaama F, Lammari K and Draoui B, 2008. Greenhouse air temperature control using fuzzy PID+I and Neuron fuzzy hybrid system controller. Proceedings of the International Review of Automatic Control (IRE.A.CO). Chau KW, 2006. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. Journal of Hydrology, p. 363. Dhamakale SD and Patil SB, 2011. Fuzzy logic approach with microcontroller for climate controlling in green house. International

Journal of Emerging Technology and Advanced Engineering, 2, 1719. Didi Faouzi, Bibi-Triki N and Chermitti A, 2016. Optimizing the greenhouse micro-climate management by the introduction of artificial intelligence using fuzzy logic. International Journal of Computer Engineering and Technology, 7, 78-92. Didi Faouzi, Bibi-Triki N, Draoui B and Abène A, 2016, Comparison of modeling and simulation results management micro climate of the greenhouse by fuzzy logic between a wetland and arid region. International Journal of Multidisciplinary Research and Modern Education (IJMRME) ISSN (Online): 2454 - 6119 II, II. Draoui B, Bounaama F, Boulard T and Bibi-Triki N, 2013. In-situ modelisation of a greenhouse climate including sensible heat, water vapour and CO2 balances. EPS Web Conferences, 45: 0102301023. DOI: 10.1051/epjconf/20134501023. El Aoud MM and Maher M, 2014. Intelligent control for a greenhouse climate. Int. J. Advances Eng. Technology, 7: 11911205. Geethanjali M, Mary Raja Slochanal S and Bhavani R, 2007. PSO trained ANN-based differential protection scheme for power transformers, Neurocomputing, in press (corrected proof). Gurbaoui M, Ed-Dahhak A, Elafou Y, Lachhab A and Belkoura L, 2013. Implementation of direct fuzzy controller in greenhouse based on labview. International Journal of Electrical and Electronics Engineering Studies, 1, 1-13. Hasni A, Draoui B, Boulard T, Taibi Rand and Dennai B, 2009. A particle swarm optimization of natural ventilation parameters in a greenhouse with continuous roof vents. Sensor Transducers J., 102: 84-93. Jamisson MH, 2006. Dynamic modeling of tree growth and energy use in a nursery greenhouse using MTLAB and Simulink. Cornell University, 7, 31. Liao CJ, Tseng CT and Luarn P, 2007.A discrete version of particle swarm optimization for flow shop scheduling problems. Computer & Operations Research, 34, 3099-111. Tasgetiren MF, Liang Y-C, Sevkli M and Gencyilmaz G, 2007.A particle swarm optimization algorithm for make span and total flow time minimization in permutation flow shop sequencing problem. European Journal of Operational Research, 177, 1930-47. Yih-Lon Lin and al., 2007.A particle swarm optimization approach to nonlinear rational filter modeling. Expert Systems with Applications(in press) (corrected proof).

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AGRICULTURAL SCIENCE AND TECHNOLOGY, VOL. 9, No 2, 2017

CONTENTS

1/2

Review Alternatives for optimisation of rumen fermentation in ruminants T. Slavov

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Genetics and Breeding Characterization of a new winter malting barley cultivar Ahil B. Dyulgerova, D. Vulchev, T. Popova Evaluation of high yielding mutants of Hordeum vulgare cultivar Izgrev B. Dyulgerova, N. Dyulgerov

98 103

Nutrition and Physiology In vitro gas production of different feeds and feed ingredients at ruminants E. Videv, J. Krastanov, S. Laleva, Т. Angelova, M. Oblakova, N. Oblakov, D. Yordanova, V. Karabashev

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Evaluation of chemical composition of raw and processed tropical sickle pod (Senna obtusifolia) seed meal Augustine C., Kwari I.D., Igwebuike J.U., Adamu S.B.

110

Effect of urea-fortified all concentrate corncob diets on serum biochemical and hematological indices of 114 West African dwarf goats U. M. Kolo, A. A. Adeloye, M. B. Yousuf

Production Systems Analysis of the technological dairy cows traffic "to and from" herringbone milking parlors K. Peychev, D. Georgiev, V. Dimova, V. Georgieva

119

Effect of pre-sowing soil tillage for wheat on the crop structure and the yield components under the conditions of slightly leached chernozem soil in Dobrudzha region P. Yankov, M. Drumeva

124

Study on the process of unloading grain harvesters at the end of the field G. Tihanov

129

Agriculture and Environment Modeling and simulation of fuzzy logic controller for optimization of the greenhouse microclimate 132 management Didi Faouzi, N. Bibi-Triki, B. Draoui, A. Abene

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CONTENTS

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Floristic diversity of 'Chinarite' protected area – Rodopi municipality, Bulgaria L. Dospatliev, M. Lacheva

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Heavy metal pools in urban soils from city parks of Sofia, Bulgaria V. G. Kachova, I. D. Atanassova

144

Ecological characteristics of reclaimed areas in Pernik mines region, Bulgaria I. Kirilov, M. Banov

151

Reclamation of soil excavated from construction and mine searching areas in Turkey F. Apaydin

160

Product Quality and Safety Concentration of sulfur-containing amino acids at turkey broilers during and after muscle dystrophy, fed with deficient feed supplemented with oxidised fat K. Stoyanchev

164

Exopolysaccharide influence's on acid gel formation K. Yoanidu, P. Boyanova, P. Panayotov

167

Carcass characteristics and technological properties of Musculus Longissimus Lumborum at lambs from the Bulgarian dairy synthetic population and its F1 crosses with meat breeds N. Ivanov, T. Angelova, S. Laleva S. Ribarski, D. Miteva, D. Yordanova, V. Karabashev, I. Penchev

171

Instruction for authors Preparation of papers Papers shall be submitted at the editorial office typed on standard typing pages (A4, 30 lines per page, 62 characters per line). The editors recommend up to 15 pages for full research paper ( including abstract references, tables, figures and other appendices) The manuscript should be structured as follows: Title, Names of authors and affiliation address, Abstract, List of keywords, Introduction, Material and methods,Results, Discussion, Conclusion, Acknowledgements (if any), References, Tables, Figures. The title needs to be as concise and informative about the nature of research. It should be written with small letter /bold, 14/ without any abbreviations. Names and affiliation of authors The names of the authors should be presented from the initials of first names followed by the family names. The complete address and name of the institution should be stated next. The affiliation of authors are designated by different signs. For the author who is going to be corresponding by the editorial board and readers, an E-mail address and telephone number should be presented as footnote on the first page. Corresponding author is indicated with *. Abstract should be not more than 350 words. It should be clearly stated what new findings have been made in the course of research. Abbreviations and references to authors are inadmissible in the summary. It should be understandable without having read the paper and should be in one paragraph. Keywords: Up to maximum of 5 keywords should be selected not repeating the title but giving the essence of study. The introduction must answer the following questions: What is known and what is new on the studied issue? What necessitated the research problem, described in the paper? What is your hypothesis and goal ? Material and methods: The objects of research, organization of experiments, chemical analyses, statistical and other methods and conditions applied for the experiments should be described in detail. A criterion of sufficient information is to be possible for others to repeat the experiment in order to verify results. Results are presented in understandable

tables and figures, accompanied by the statistical parameters needed for the evaluation. Data from tables and figures should not be repeated in the text. Tables should be as simple and as few as possible. Each table should have its own explanatory title and to be typed on a separate page. They should be outside the main body of the text and an indication should be given where it should be inserted. Figures should be sharp with good contrast and rendition. Graphic materials should be preferred. Photographs to be appropriate for printing. Illustrations are supplied in colour as an exception after special agreement with the editorial board and possible payment of extra costs. The figures are to be each in a single file and their location should be given within the text. Discussion: The objective of this section is to indicate the scientific significance of the study. By comparing the results and conclusions of other scientists the contribution of the study for expanding or modifying existing knowledge is pointed out clearly and convincingly to the reader. Conclusion: The most important consequences for the science and practice resulting from the conducted research should be summarized in a few sentences. The conclusions shouldn't be numbered and no new paragraphs be used. Contributions are the core of conclusions. References: In the text, references should be cited as follows: single author: Sandberg (2002); two authors: Andersson and Georges (2004); more than two authors: Andersson et al.(2003). When several references are cited simultaneously, they should be ranked by chronological order e.g.: (Sandberg, 2002; Andersson et al., 2003; Andersson and Georges, 2004). References are arranged alphabetically by the name of the first author. If an author is cited more than once, first his individual publications are given ranked by year, then come publications with one co-author, two co-authors, etc. The names of authors, article and journal titles in the Cyrillic or alphabet different from Latin, should be transliterated into Latin and article titles should be translated into English. The original language of articles and books translated into English is indicated in parenthesis after the bibliographic reference (Bulgarian = Bg, Russian = Ru, Serbian = Sr, if in the Cyrillic, Mongolian =

Мо, Greek = Gr, Georgian = Geor., Japanese = Jа, Chinese = Ch, Arabic = Аr, etc.) The following order in the reference list is recommended: Journal articles: Author(s) surname and initials, year. Title. Full title of the journal, volume, pages. Example: Simm G, Lewis RM, Grundy B and Dingwall WS, 2002. Responses to selection for lean growth in sheep. Animal Science, 74, 39-50 Books: Author(s) surname and initials, year. Title. Edition, name of publisher, place of publication. Example: Oldenbroek JK, 1999. Genebanks and the conservation of farm animal genetic resources, Second edition. DLO Institute for Animal Science and Health, Netherlands. Book chapter or conference proceedings:

Author(s) surname and initials, year. Title. In: Title of the book or of the proceedings followed by the editor(s), volume, pages. Name of publisher, place of publication. Example: Mauff G, Pulverer G, Operkuch W, Hummel K and Hidden C, 1995. C3variants and diverse phenotypes of unconverted and converted C3. In: Provides of the Biological Fluids (ed. H. Peters), vol. 22, 143-165, Pergamon Press. Oxford, UK. Todorov N and Mitev J, 1995. Effect of level of feeding during dry period, and body condition score on reproductive performance in dairy cows,IXth International Conference on Production Diseases in Farm Animals, September 11–14, Berlin, Germany. Thesis: Hristova D, 2013. Investigation on genetic diversity in local sheep breeds using DNA markers. Thesis for PhD, Trakia University, Stara Zagora, Bulgaria, (Bg). The Editorial Board of the Journal is not responsible for incorrect quotes of reference sources and the relevant violations of copyrights. Animal welfare Studies performed on experimental animals should be carried out according to internationally recognized guidelines for animal welfare. That should be clearly described in the respective section “Material and methods”.

Volume 9, Number 2 June 2017

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