ANN based modeling and control of GHS for winter climate

Manonmani, A.; Thyagarajan, T.; Subbian, Sutha

doi:10.1109/tima.2017.8064816

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…To fulfil these needs, several control strategies including complex and sophisticated algorithms have been developed and discussed in the literature, such as fuzzy systems that have obtained considerable benefits. Some researchers have been interested in the use of adaptive predictive command, proportional-integral-derivative controllers (Ge et al, 2016;Ma et al, 2019), PI-SSOD and SSODPI techniques (Malathi et al, 2017), the genetic algorithm (Manonmani et al, 2017), and nonlinear adaptive PID control (Maurya & Jain, 2016). However, these techniques need to be enhanced to improve the control efficiency of their systems.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of FLC Applied to a Smart Greenhouse

2020

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Greenhouse cultivation needs an accurate modelling and an optimised control system to ensure an adequate microclimatic condition for plants. It represents a complex task due to the high number of input factors. For this reason, an improved intermediate modelling was established under the platform of the MATLAB/Simulink environment to simulate the energy balance and the fuzzy logic controller (FLC) in order to promote a suitable microclimate through the control of the relevant actuators which have been installed in the greenhouse. In addition, the control of the system has been improved through the integration of the Internet of things for data monitoring and recording in real time. The system was designed, prototyped, and tested in Tunis province of Tunisia. Our contribution through this work represents the implementation of the FLC on a Raspberry Pi 3, and the management of the agriculture through the use of a digital innovation (IoT). All this has allowed us not only to supervise, control and reduce the energy cost of electrical load but also to improve the productivity and quality of the greenhouse cultivation.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of FLC Applied to a Smart Greenhouse

2020

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“…As for ANN-based algorithms, they have a great capacity to detect and model non-linear [18]as well as complex relationships between different variables. In [19]- [20], ANN controllers for greenhouse climate have been studied. However, this kind of controllers suffers from computational cost because it needs massive training data [21].…”

Section: Introductionmentioning

confidence: 99%

Intelligent control for an experimental greenhouse climate based on ANFIS Technology

Oubeha¹

2020

IJATCSE

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This study developed an intelligent control system to improve the climate management under greenhouse. The proposed regulation structure relies on an adaptive neuro-fuzzy inference system (ANFIS) inverse model. At first, the controller was trained with practical database using Matlab tool. Afterwards, it was simulated through a greenhouse model. Finally, it has been validated in real time based on an automated experimental greenhouse. Simulation studies and experimental results proved that the adopted control strategy is able to yield good regulation despite of the greenhouse system complexity and the continuous changes of environmental conditions. Moreover, employing our strategy reduced settling time compared to PID and fuzzy based controllers which are commonly utilized in the control engineering field.

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“…The method they developed provided 22% savings on energy efficiency and 33% savings on water use. Manonmani et al [9] controlled the temperature and humidity values of a greenhouse by developing an artificial neural network (ANN) model to produce efficient and high-quality pepper cultivation in a greenhouse environment. It was assumed that the temperature of the greenhouse was between approximately 32°C and 35°C for winter conditions and the moisture value was between 12 and 8 g/kg.…”

Section: Introductionmentioning

confidence: 99%

The Control of Greenhouses Based on Fuzzy Logic Using Wireless Sensor Networks

Alpay¹,

Erdem²

2019

IJCIS

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Wireless sensor network Fuzzy logic Internet of things Smart greenhouse Greenhouse climate control A B S T R A C T ID:p0060Greenhouses cannot be easily controlled because their climate parameters are interrelated. This study contributes to increasing the quality and yield of greenhouses by saving time, energy, light and water consumption via measuring and controlling the climate parameters that are effective in forming climate factors in greenhouses. The greenhouse climate variables including temperature, relative humidity, soil moisture and light intensity were measured by a realistic sensor application. In this way, several sensor nodes, that belong to the nodal packages were distributed to a wireless sensor network (WSN) constructed in a star topology. In addition, the data obtained from the nodes, have been controlled and monitored with the fuzzy logic-based control strategy proposed as a developing, smart and remotely accessible Android-based interface. The proposed method has been analyzed, and its performances have been evaluated in terms of the benefits of both the user and the greenhouse.

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ANN based modeling and control of GHS for winter climate

Cited by 4 publications

References 9 publications

Design and Implementation of FLC Applied to a Smart Greenhouse

Design and Implementation of FLC Applied to a Smart Greenhouse

Intelligent control for an experimental greenhouse climate based on ANFIS Technology

The Control of Greenhouses Based on Fuzzy Logic Using Wireless Sensor Networks

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