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Special Issue

A Modeling Approach to Automate the Functioning of Tomato Greenhouses

Document Type : Research paper

Authors

1 Laboratory of soft material and Electromagnetic modeling, Faculty of Science Tunis, University Tunis El Manar, Tunis, Tunisia, Department of Hydraulic, High School of Engineering, Medjez El Bab, IRESA, University of Jendouba, Jendouba, Tunisia

2 Faculty of Science Bizerte, University of Carthage, Bizerte, Tunisia

10.22059/ijhst.2022.327568.485

Abstract

Farmers and experts are continuously searching for optimal conditions to improve the productivity of tomatoes in greenhouses. To provide an answer to their concerns, we have developed a modeling approach to automate the functioning of a greenhouse cultivated with tomato plants. For the aeration, heating, and irrigation systems, we compared the Proportional Integral Derivative (PID) controller response to the Fuzzy logic (FL) controller response. For the aeration system, the response of both controllers was stable, with a pick of about 1.09 for the PID controller and zero for the fuzzy controller. Likewise, there was no overtaking for the fuzzy controller but about 8.28% for the PID controller. The rise time for the fuzzy controller was less than that of the PID controller (627 s). We signaled a stable response for the PID controller and the fuzzy logic controller for the irrigation system. The pick and the overtake were equal to zero for the fuzzy logic controller but were 1.28 and 28.2 s for the PID controller, respectively. In the case of both controllers, the rise time was the same, equaling 18.3 s. The regulation time was less than 35 s for the fuzzy logic controller and 31.1 s for the PID controller.

Keywords

References
Altas IH, Sharaf AM. 2007. A generalized direct approach for designing fuzzy logic controllers in Matlab/Simulink GUI environment. International Journal of Information Technology and Intelligent Computing 4(1), 27.
Anamekere IJ, Harrison OE, Enyenihi HJ. 2019. Modeling of automatic sprinkler irrigation process using finite state machine (FSM) and proportional integral derivative (PID) controller. Universal Journal of Engineering Science 7(4), 75-81.
Araujo EF, Mauri AL, Araujo RF, Ribeiro Amaro HT, Henriques da Silva DJ. 2018. Physiological and sanitary quality of organic tomato seeds treated with clove basil extracts. Communicate Scientiae 9(1), 6-33.
Ben Aich F, Ben Farhat M, Ouni A, Ben Ahmed H, Chalh A. 2019. Differentiation of salt stress tolerance of six varieties of tomato (Solanum Lycopersicum L.) based on physiological factors. Vie et Milieu- Life and Environnent 69(4), 215-223.
Ben Aîch F. 2020. Effects of salinity on the physiological and metabolic behavior of three Solanaceae (tomato, pepper, and eggplant) and improvement of their tolerance by physiological means. Ph.D. Thesis, Faculty of Sciences, University Tunis El Manar, Tunisia.
Ben Ali R, Bouadila S, Mami AK. 2018. Development of a fuzzy logic controller applied to an agricultural greenhouse experimentally validated. Applied Thermal Engineering 141, 798-810.
Branthôme F-Xa. 2020. Global Consumption of Tomato Products. Editor: Tomato News (Western Pacific Trust Company funding). p.14.
Carlos RA, Jesús CC, Aura PL. 2017. Low-cost fuzzy logic control for greenhouse environments with web monitoring. Electronics 6(71), 12.
Chen PC, Mills JK. 1997. Synthesis of neural networks and PID control for performance improvement of industrial robots. Journal of Intelligent and Robotic Systems 20(2–4), 157–80.
Dehnavard S, Souri MK, Mardanlu S. 2017. Tomato growth responses to foliar application of ammonium sulfate in hydroponic culture. Journal of Plant Nutrition 40(3), 315-323.
Didi F, Bibi-Triki N, Draoui B, Abène A. 2017. Modeling a fuzzy logic controller to simulate and optimize the greenhouse microclimate management using MATLAB SIMULINK. International Journal Mathematical Sciences and Computing 3, 12-27.
Dos Santos C. 2009. Tuning of PID controller for an automatic regulator voltage system using a chaotic optimization approach. Chaos, Solitons and Fractals 39, 1504-1515.
Eddahhar A, Guerbaoui M, Elafou Y, Outanoute M, Lachhab A, Belkoura L, Bouchikhi B. 2013. Implementation of the fuzzy controller to reduce water irrigation in a greenhouse using LabVIEW. International Journal of Engineering and Advanced Technology Studies 1(2), 12-22.
Fuseini SI, Dominic K, Stephen M. 2018. Smart irrigation system using a fuzzy logic method. International Journal of Engineering Research and Technology 11(9), 1417-1436.
Gadelhag M, Ahmad L, Pourabdollah A. 2018. Human activities recognition based on neuro-fuzzy finite state machine. Technologies 6(4), 110.
Gharbi E, Martínez JP, Benahmed H, Lepoint G, Vanpee B, Quinet M, Lutts S. 2017. Inhibition of ethylene synthesis reduces salt tolerance in tomato wild relative species Solanum chilense. Journal of Plant Physiology 210, 24–37.
Goodchild MS, Kühn KD, Jenkins MD, Burek KJ, Dutton AJ. 2015. A method for precision closed-loop irrigation using a modified PID control algorithm. Sensors and Transducers 188(5), 61-68.
Ho WK, Hang CC, Cao LS. 1995. Tuning of PID controllers based on gain and phase margin specifications. Automatica 31(3), 497–502.
Hung ML, Lin JS, Yan JJ, Liao TL. 2008. Optimal PID control design for synchronization of delayed discrete chaotic systems. Chaos, Solitons and Fractals 35(4), 781–785.
Izzuddin TA, Johari MA, Rashid MZA, Jali MH. 2018. Smart irrigation using the fuzzy logic method. Journal of Engineering and Applied Sciences 13(2), 515-522.
Jomaa M, Abbes M, Tadeo F, Mami A. 2019. Greenhouse modeling, validation, and climate control based on fuzzy logic. Engineering, Technology and Applied Science Research 9(4), 4405-4410.
Kao CC, Chuang CW, Fung RF. 2006. The self-tuning PID control in a slider-crank mechanism system by applying particle swarm optimization approach. Mechatronics 16(8), 513–22.
Kiranpreet K, Vikram M, Inderjeet SiG. 2010. Fuzzy logic-based image edge detection algorithm in MATLAB. International Journal of Computer Applications 1(22), 55-58.
Mansour HA, Eldardiry EI, Abd El-Hady M. 2018. Validation by aqua crop model for faba bean yield, water productivity under the smart controller of drip irrigation system, and compost fertilizer in sandy soil. European Journal of Academic Essays 5(5), 138-144.
Mansour HA, Pibars SK, Gaballah MS. 2018. Effect of the smart drip irrigation system and water deficit on contour maps of soil moisture distribution. Worldwide Journal of Multidisciplinary Research and Development 4(2), 313-320.
Martinez Baquero JE, Corredor Chavarr FA, Jimenez Moreno R. 2018. Design of controller PID and stability analysis for drying of corn grains. International Journal of Applied Engineering Research 13(21), 15410– 15416.
Mattara CS, Abhilasha S, Venkatesan K, Ramanujam K, Chinnapalaniandi P. 2020 Design and robustness analysis of intelligent controllers for commercial greenhouses. Mechanical Sciences 11, 299–316.
Oboulbiga EB, Parkouda C, Sawadogo-Lingani H, Compaoré Ella WR, Sakira AK, Traoré AS. 2017. Nutritional composition, physical characteristics, and sanitary quality of the tomato variety mongol F1 from Burkina Faso. Food and Nutrition Sciences 8, 444-455.
Özlem A, Ebubekir E. 2019. The control of greenhouses based on fuzzy logic using wireless sensor networks. International Journal of Computational Intelligence Systems 12(1), 190–203.
Rafiuddin S, Wahyu H, Piarah BJ. 2015. Controlling smart greenhouse using the fuzzy logic method. International Journal on Smart Material and Mechatronics 2(2), 116-120.
Rajaprakash S, Jaichandran R, Ramalingam P, Nagappan A. 2017. Fuzzy logic controller for effective irrigation based on field soil moisture and availability of water. Journal of Advance Research in Dynamical and Control Systems 9(1), 90-97.
Riahi J, Vergura S, Mezghani D, Mami AK. 2020. Intelligent control of the microclimate of an agricultural greenhouse powered by a supporting PV system. Applied Sciences 10(4), 1350.
Rouphael Y, Colla G, Giordano M, El-Nakhel C, Kyriacou MC, De Pascale S. 2017. Foliar applications of a legumederived protein hydrolysate elicit dose-dependent increases in growth, leaf mineral composition, yield, and fruit quality in two greenhouse tomato cultivars. Sciences Horticulturae 226, 353–360.
Sahbani F, Ferjani E. 2018. Identification and modeling of drop-by-drop irrigation system for tomato plants under greenhouse conditions. Irrigation and Drainage 67(4), 550-558.
Sahbani F, Sellami MH. 2020. Experimental analysis of the effect of deficit irrigation on the productivity of a greenhouse tomato crop in Sejnane, Tunisia. International Journal of Agriculture, Environment and Bioresearch 5(05), 30-45.
Satyajit Ramesh P, Chandrakant Balkrishna P, Ravindra Ramchandra M. 2017. Greenhouse air-temperature modeling and fuzzy logic control. International Journal of Electronics Engineering Research 9(5), 727-734.
Sivanandam SN, Sumathi S, Deepa SN. 2007. Introduction to fuzzy logic using MATLAB. SpringerVerlag Berlin Heidelberg. p. 419.
Smriti KR, Ravi M. 2014. Comparative study of P, PI, and PID controller for speed control of VSI-fed induction motor. International Journal of Engineering Development and Research 2(2), 2740-2744.
Souri MK, Dehnavard S. 2018. Tomato plant growth, leaf nutrient concentrations, and fruit quality under nitrogen foliar applications. Advances in Horticultural Science 32(1), 41-47. Souri MK, Bakhtiarizade M. 2019. Biostimulation effects of rosemary essential oil on growth and nutrient uptake of tomato seedlings. Scientia Horticulture 243, 472-476.
Varun K. 2018. Application of fuzzy logic in the water irrigation system. International Research Journal of Engineering and Technology 5(4), 3372-3375.
Willians R, Fábio Meneghetti UA, Ritaban D, Derek MH. 2019. Fuzzy control system for variable rate irrigation using remote sensing. Expert Systems with Applications 124, 13-24.
Yager RR, Filev DP. 1994. Essentials of fuzzy modeling and control. Special Interest Group on Artificial Intelligence Bulletin 6(4), 22-23.
International Journal of Horticultural Science and Technology
Volume 10, Issue 4
October 2023
Pages 387-396
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History
  • Receive Date: 31 August 2022
  • Revise Date: 22 October 2022
  • Accept Date: 28 October 2022
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