Data-driven evaluation of machine learning models for climate control in operational smart greenhouses

Nowadays, human overpopulation is stressing our ecosystems in different ways, agriculture being a critical example as different predictions point towards food shortages in the near future. Accordingly, smart farming is becoming key to the optimization of natural resources so that different crops can...

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Autores: Morales García, Juan, Bueno Crespo, Andrés, Martínez España, Raquel, Cecilia Canales, José María
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Católica San Antonio de Murcia (UCAM)
Repositorio:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
OAI Identifier:oai:repositorio.ucam.edu:10952/7390
Acceso en línea:http://hdl.handle.net/10952/7390
Access Level:acceso abierto
Palabra clave:Precision Agriculture
Artificial Intelligence
Machine Learning
Temperature Forecasting
Smart Greenhouses
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spelling Data-driven evaluation of machine learning models for climate control in operational smart greenhousesMorales García, JuanBueno Crespo, AndrésMartínez España, RaquelCecilia Canales, José MaríaPrecision AgricultureArtificial IntelligenceMachine LearningTemperature ForecastingSmart GreenhousesNowadays, human overpopulation is stressing our ecosystems in different ways, agriculture being a critical example as different predictions point towards food shortages in the near future. Accordingly, smart farming is becoming key to the optimization of natural resources so that different crops can be grown efficiently, consuming as few resources as possible. In particular, greenhouses have proved to be an effective way of producing a high volume of vegetables/fruits in a reduced space and within a short time span. Hence, optimizing greenhouse functioning results in less water use and nutrient consumption, less energy use, faster growth, and better product quality. In this article, we carry out an in-depth analysis of different machine learning (ML) models to improve climate control in smart greenhouses. As part of the analysis of the techniques we also considered 3 ways of pre-processing the data, as well as 12-hour and 24-hour forecasting. We focus on forecasting the indoor air temperature of an operational smart greenhouse, i.e. assessing the data anomalies that are inherently present in these environments due to the instability of IoT infrastructures. Several ML models are adapted to time series forecasting to provide an overview of these techniques and to find out which one performs better in this particular scenario. Our results show that, after statistically validating the results, the Random Forest Regression technique gives the best overall result with a mean absolute error of less than 1 degree Celsius.Ingeniería, Industria y Construcción2023info:eu-repo/semantics/articlehttp://hdl.handle.net/10952/7390reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murciainstname:Universidad Católica San Antonio de Murcia (UCAM)Inglésinfo:eu-repo/semantics/openAccessoai:repositorio.ucam.edu:10952/73902026-06-07T18:35:21Z
dc.title.none.fl_str_mv Data-driven evaluation of machine learning models for climate control in operational smart greenhouses
title Data-driven evaluation of machine learning models for climate control in operational smart greenhouses
spellingShingle Data-driven evaluation of machine learning models for climate control in operational smart greenhouses
Morales García, Juan
Precision Agriculture
Artificial Intelligence
Machine Learning
Temperature Forecasting
Smart Greenhouses
title_short Data-driven evaluation of machine learning models for climate control in operational smart greenhouses
title_full Data-driven evaluation of machine learning models for climate control in operational smart greenhouses
title_fullStr Data-driven evaluation of machine learning models for climate control in operational smart greenhouses
title_full_unstemmed Data-driven evaluation of machine learning models for climate control in operational smart greenhouses
title_sort Data-driven evaluation of machine learning models for climate control in operational smart greenhouses
dc.creator.none.fl_str_mv Morales García, Juan
Bueno Crespo, Andrés
Martínez España, Raquel
Cecilia Canales, José María
author Morales García, Juan
author_facet Morales García, Juan
Bueno Crespo, Andrés
Martínez España, Raquel
Cecilia Canales, José María
author_role author
author2 Bueno Crespo, Andrés
Martínez España, Raquel
Cecilia Canales, José María
author2_role author
author
author
dc.subject.none.fl_str_mv Precision Agriculture
Artificial Intelligence
Machine Learning
Temperature Forecasting
Smart Greenhouses
topic Precision Agriculture
Artificial Intelligence
Machine Learning
Temperature Forecasting
Smart Greenhouses
description Nowadays, human overpopulation is stressing our ecosystems in different ways, agriculture being a critical example as different predictions point towards food shortages in the near future. Accordingly, smart farming is becoming key to the optimization of natural resources so that different crops can be grown efficiently, consuming as few resources as possible. In particular, greenhouses have proved to be an effective way of producing a high volume of vegetables/fruits in a reduced space and within a short time span. Hence, optimizing greenhouse functioning results in less water use and nutrient consumption, less energy use, faster growth, and better product quality. In this article, we carry out an in-depth analysis of different machine learning (ML) models to improve climate control in smart greenhouses. As part of the analysis of the techniques we also considered 3 ways of pre-processing the data, as well as 12-hour and 24-hour forecasting. We focus on forecasting the indoor air temperature of an operational smart greenhouse, i.e. assessing the data anomalies that are inherently present in these environments due to the instability of IoT infrastructures. Several ML models are adapted to time series forecasting to provide an overview of these techniques and to find out which one performs better in this particular scenario. Our results show that, after statistically validating the results, the Random Forest Regression technique gives the best overall result with a mean absolute error of less than 1 degree Celsius.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10952/7390
url http://hdl.handle.net/10952/7390
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
instname:Universidad Católica San Antonio de Murcia (UCAM)
instname_str Universidad Católica San Antonio de Murcia (UCAM)
reponame_str RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
collection RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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