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A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network

In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in...

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Autores principales: Guesbaya, Mounir, García-Mañas, Francisco, Rodríguez, Francisco, Megherbi, Hassina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921858/
https://www.ncbi.nlm.nih.gov/pubmed/36772289
http://dx.doi.org/10.3390/s23031250
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author Guesbaya, Mounir
García-Mañas, Francisco
Rodríguez, Francisco
Megherbi, Hassina
author_facet Guesbaya, Mounir
García-Mañas, Francisco
Rodríguez, Francisco
Megherbi, Hassina
author_sort Guesbaya, Mounir
collection PubMed
description In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate.
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spelling pubmed-99218582023-02-12 A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network Guesbaya, Mounir García-Mañas, Francisco Rodríguez, Francisco Megherbi, Hassina Sensors (Basel) Article In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate. MDPI 2023-01-21 /pmc/articles/PMC9921858/ /pubmed/36772289 http://dx.doi.org/10.3390/s23031250 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guesbaya, Mounir
García-Mañas, Francisco
Rodríguez, Francisco
Megherbi, Hassina
A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network
title A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network
title_full A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network
title_fullStr A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network
title_full_unstemmed A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network
title_short A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network
title_sort soft sensor to estimate the opening of greenhouse vents based on an lstm-rnn neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921858/
https://www.ncbi.nlm.nih.gov/pubmed/36772289
http://dx.doi.org/10.3390/s23031250
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