An intelligent system for the climate control and energy savings in agricultural greenhouses

Azaza, Maher; Echaieb, Kamel; Fabrizio, Enrico; Iqbal, Atif; Mami, Abdelkader

doi:10.1007/s12053-015-9421-8

Cited by 55 publications

(37 citation statements)

References 30 publications

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“…approach achieves better performances with respect to both method and results shown on [23] and to the "traditional" control approach, (such as a basic thermostat). The combination of ANN and parallel fuzzy scheme, allows us to dynamically respond to outdoor and indoor climatic variations.…”

Section: # Input Neurons Nnarx # Neurons In Hidden Layermentioning

confidence: 94%

“…When the controller parameters exceed the target value by too much, the system runs at maximum air speed (in the simulated scenario the maximum value is set) and its temperature is very low (in the simulated scenario 5 • C) in order to keep the internal temperature to the target value. Figure 8 shows also a comparison with results on [23]. Although [23] is probably the most influential literature work that addresses smart climatic control in greenhouses, that work is focused on energy management through photovoltaic energy in order to minimize the use of conventional electrical grid and to lower costs of agriculture production.…”

Section: # Input Neurons Nnarx # Neurons In Hidden Layermentioning

confidence: 99%

“…In fact, the use of smart setting and tuning techniques for these controllers can improve the crop production as demonstrated by authors in [23]. For this reason, the FLCs, based on linguistic rules instead of inflexible reasoning, can be the right choice to describe greenhouse systems and to increase crop production.…”

Section: Introductionmentioning

confidence: 99%

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An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

2017

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Abstract:In the recent past home automation has been expanding its objectives towards new solutions both inside the smart home and in its outdoor spaces, where several new technologies are available. This work has developed an approach to integrate intelligent microclimatic greenhouse control into integrated home automation. Microclimatic control of greenhouses is a critical issue in agricultural practices, due to often common sudden daily variation of climatic conditions, and to its potentially detrimental effect on plant growth. A greenhouse is a complex thermodynamic system where indoor temperature and relative humidity have to be closely monitored to facilitate plant growth and production. This work shows an adaptive control system tailored to regulate microclimatic conditions in a greenhouse, by using an innovative combination of soft computing applications. In particular, a neural network solution has been proposed in order to forecast the climatic behavior of greenhouse, while a parallel fuzzy scheme approach is carried out in order to adjust the air speed of fan-coil and its temperature. The proposed combined approach provides a better control of greenhouse climatic conditions due to the system's capability to base instantaneous solutions both on real measured variables and on forecasted climatic change. Several simulation campaigns were carried out to perform accurate neural network and fuzzy schemes, aimed at obtaining respectively a minimum forecasted error value and a more appropriate fuzzification and de-fuzzification process. A Matlab/Simulink solution implemented with a combined approach and its relevant obtained performance is also shown in present study, demonstrating that through controlled parameters it will be possible to maintain a better level of indoor climatic conditions. In the present work we prove how with a forecast of outside temperature at the next time-instant and rule-based controller monitoring of cooling or heating air temperatures and air velocities of devices that regulate the indoor micro-climate inside, a better adjustment of the conditions of comfort for crops is achievable.

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Section: # Input Neurons Nnarx # Neurons In Hidden Layermentioning

confidence: 94%

Section: # Input Neurons Nnarx # Neurons In Hidden Layermentioning

confidence: 99%

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An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

2017

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“…The ventilation rate depends on the fan speed, so the desired speed is the input of the vector control. The fan speed is calculated by the linear fan law described by the equation [32]…”

Section: Vector Control Optimized By Fuzzy Logicmentioning

confidence: 99%

Intelligent Control of the Microclimate of an Agricultural Greenhouse Powered by a Supporting PV System

et al. 2020

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An agricultural greenhouse is a complex and Multi-Input Multi-Output MIMO system in which the internal parameters create a favorable microclimate for agricultural production. Temperature and internal humidity are two parameters that have a major impact on greenhouse yield. The objective of this study was to propose a simulated dynamic model in a MATLAB/Simulink environment for experimental validation. Moreover, a fuzzy controller was designed to manage a greenhouse indoor climate by means of an asynchronous motor for ventilation, heating, humidification, etc. An intelligent system to control these actuators for an optimal inside climate was implemented in the model. The dynamic model was validated by comparing the simulation results to experimental measurements. These results showed the effectiveness of the control strategy in regulating the greenhouse indoor climate. Finally, a photovoltaic generator was modeled, with the aim of reducing the costs of agricultural production. It feeds the asynchronous motor with a vector control optimized by fuzzy logic that drives a variable speed fan.

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“…In addition, research has been conducted using different versions of fuzzy controllers such as traditional [5], inverted [6], adaptive [7], and improved via particle swarm optimization [8]. It also highlights the growing use of neural networks for smart frost control [9], the dynamic modeling of temperature and relative humidity [10], and climate control and energy saving in different types of greenhouses [11].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Fuzzy Logic Control for Greenhouse Environments with Web Monitoring

Robles-Algarín¹,

Cabarcas²,

Llanos³

2017

Electronics

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Abstract:The design and implementation of a low-cost system for monitoring and remote control of a greenhouse using fuzzy logic is presented. For the control system, an Arduino Mega board was programmed with a fuzzy algorithm to monitor and perform control actions for environmental temperature, soil moisture, relative humidity, and lighting. A website was designed to visualize the main indicators of agricultural interest and to get access to tools such as forced ventilation, misting systems, and sprinkler irrigation. For connectivity to the webpage, an Arduino Ethernet Shield was used. Thus, it was possible to establish a local area network and monitor and control the greenhouse climate variables manually or automatically. The application designed allowed access to the configuration, monitoring, and control of climatic conditions in the greenhouse. The effectiveness of fuzzy logic to control nonlinear systems was therefore verified without the mathematical model of the plant. Thus, the use of resources for a gable roof greenhouse prototype was optimized.

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An intelligent system for the climate control and energy savings in agricultural greenhouses

Cited by 55 publications

References 30 publications

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

Intelligent Control of the Microclimate of an Agricultural Greenhouse Powered by a Supporting PV System

Low-Cost Fuzzy Logic Control for Greenhouse Environments with Web Monitoring

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