A Simulation Based Study of A Greenhouse System with Intelligent Fuzzy Logic

Abstract

Greenhouse (GHS) system is a system, that provides an efficient condition to grow plants. This research paper describes designing of a greenhouse system to control climate, soil moisture, lighting using fuzzy logic. The proposed model consists fuzzy logic to control GHS parameter such as temperature, Humidity, light, soil moisture and watering system to the plant. In this proposed system temperature controlling controller is used to take current temperature as input by using temperature sensor and its deviation from user set data. The temperature is controlled by the speed of fan. This algorithm is same for all other parameters. In this research the set value of different sensors is selected by the owner of the greenhouse according to the basis of growing plant condition. This system will enhance the capability of fuzzy logic control systems in case of process automation and potentiality.Simulation using MATLAB is used to achieve the designed goal. AUTHORSNiaz Mostakim has achieved his B.Sc. and M.Sc. degree from the dept. of Electrical and Electronic Engineering of Islamic University, Kushtia-7003, Bangladesh. He is a Lecturer of the department of Electrical and Electronic Engineering at Atish Dipankar University of Science and Technology, Uttara, Dhaka. His current interest includes internet of things (IOT), Telemedicine, Control System, Fuzzy controller, microcontroller based system design, electronics system design and development, Artificial intelligence.Shuaib Mahmud has achieved his B.Sc. and M.Sc. degree from the dept.

Mathematical Model of Measuring Monitoring and Temperature Control of Growing Vegetables in Greenhouses

“…In the articles [9,10] physical and mathematical foundations of forecasting the temperature dynamics in greenhouses are grounded on the basis of adaptive measuring monitoring of the greenhouse indoor microclimate and external climatic factors. In articles [11][12][13],…”

A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

“…University or institution (first author) Type Country [23] School of Science and Engineering, UNESP Public Brazil [11] Islamic Azad University Private Iran [20] Universidad Autónoma de Querétaro Public Mexico [13] Universidade de TrásosMontes e Alto Douro Public Portugal [2] University BadjiMokhtar Public Algeria [3] Don State Technical University Public Russia [4] Universidad de Valladolid Public Spain [21] Universidad del Magdalena Public Colombia [6] University of Tehran Public Iran [7] URERMS Public Algeria [18] Universidad Autónoma Chapingo Public Mexico [14] University BadjiMokhtar Public Algeria [9] Tunis El Manar University Public Tunisia [19] Birla Institute of Technology and Science Pilani Private India [15] Zhejiang Univesity City College Public China [12] University of Water Resources and Electric Power Public China [22] Shenyang University Public China [1] Zhejiang University of Technology Public China [24] Suzhou Institute of Industrial Technology Public China [28] Ehime University Public Japan [29] Beijing Institute of Technology Public China [30] Sichuan Agricultural University Public China [31] China Jiliang University Public China [32] University BadjiMokhtar Public Algeria [33] École nationale d'ingénieurs de Sfax Public Tunisia [34] Menoufia University Public Egypt [35] University of Toulon Public France [36] St John's University Private Taiwan [37] Universidad Tecnológica de Querétaro Public Mexico [38] King Abdulaziz University Public Saudi Arabia [39] Technological Educational Institute of Crete Public Greece [40] Maulana Azad National Institute of Technology Public India [41] Atish Dipankar University of Science and Technology Private Bangladesh…”

Dual stage cascade controller for temperature control in greenhouse

“…The temperature level within the greenhouse mainly affects to the physiological processes of the plants, thus, it is essential keep it in an acceptable value based on the crop difference and the geographical place of the system environment. This improves its growth and raises the production quality with reducing energy consumption [1].…”