Evaluation of low-power devices for smart greenhouse development

The combination of artificial intelligence and the Internet of Things (AIoT) is enabling the next economic revolution in which data and immediacy are at the key players. Agriculture is one of the sectors that can benefit most from the use of AIoT to optimise resources and reduce its environmental fo...

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Authors: Morales García, Juan, Bueno Crespo, Andrés, Martínez España, Raquel, Posadas Yagüe, Juan Luis, Manzoni, Pietro, Cecilia Canales, José María
Format: article
Publication Date:2023
Country:España
Institution:Universidad Católica San Antonio de Murcia (UCAM)
Repository:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
OAI Identifier:oai:repositorio.ucam.edu:10952/7385
Online Access:http://hdl.handle.net/10952/7385
Access Level:Open access
Keyword:Artificial Intelligence
Edge computing
Time-series forecast
TinyML
CPU-GPU Performance
Power consumption
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spelling Evaluation of low-power devices for smart greenhouse developmentMorales García, JuanBueno Crespo, AndrésMartínez España, RaquelPosadas Yagüe, Juan LuisManzoni, PietroCecilia Canales, José MaríaArtificial IntelligenceEdge computingTime-series forecastTinyMLCPU-GPU PerformancePower consumptionThe combination of artificial intelligence and the Internet of Things (AIoT) is enabling the next economic revolution in which data and immediacy are at the key players. Agriculture is one of the sectors that can benefit most from the use of AIoT to optimise resources and reduce its environmental footprint. However, this convergence requires computational resources that enable the execution of AI workloads, and in the context of agriculture, ensuring autonomous operation and low energy consumption. In this work, we evaluate TinyML and edge computing platforms to predict the indoor temperature of an operational greenhouse in situ. In particular, the computational/energy trade-off of these platforms is assessed to analyse whether their use in this context is feasible. Two artificial neural networks (ANNs) are adapted to these platforms to predict the indoor temperature of the greenhouse. Our results show that the microcontroller-based devices can offer a competitive and energy-efficient computational alternative to more traditional edge computing approaches for lightweight ML workloads.Ingeniería, Industria y Construcción2023info:eu-repo/semantics/articlehttp://hdl.handle.net/10952/7385reponame: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:10952/73852026-06-07T18:35:21Z
dc.title.none.fl_str_mv Evaluation of low-power devices for smart greenhouse development
title Evaluation of low-power devices for smart greenhouse development
spellingShingle Evaluation of low-power devices for smart greenhouse development
Morales García, Juan
Artificial Intelligence
Edge computing
Time-series forecast
TinyML
CPU-GPU Performance
Power consumption
title_short Evaluation of low-power devices for smart greenhouse development
title_full Evaluation of low-power devices for smart greenhouse development
title_fullStr Evaluation of low-power devices for smart greenhouse development
title_full_unstemmed Evaluation of low-power devices for smart greenhouse development
title_sort Evaluation of low-power devices for smart greenhouse development
dc.creator.none.fl_str_mv Morales García, Juan
Bueno Crespo, Andrés
Martínez España, Raquel
Posadas Yagüe, Juan Luis
Manzoni, Pietro
Cecilia Canales, José María
author Morales García, Juan
author_facet Morales García, Juan
Bueno Crespo, Andrés
Martínez España, Raquel
Posadas Yagüe, Juan Luis
Manzoni, Pietro
Cecilia Canales, José María
author_role author
author2 Bueno Crespo, Andrés
Martínez España, Raquel
Posadas Yagüe, Juan Luis
Manzoni, Pietro
Cecilia Canales, José María
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Artificial Intelligence
Edge computing
Time-series forecast
TinyML
CPU-GPU Performance
Power consumption
topic Artificial Intelligence
Edge computing
Time-series forecast
TinyML
CPU-GPU Performance
Power consumption
description The combination of artificial intelligence and the Internet of Things (AIoT) is enabling the next economic revolution in which data and immediacy are at the key players. Agriculture is one of the sectors that can benefit most from the use of AIoT to optimise resources and reduce its environmental footprint. However, this convergence requires computational resources that enable the execution of AI workloads, and in the context of agriculture, ensuring autonomous operation and low energy consumption. In this work, we evaluate TinyML and edge computing platforms to predict the indoor temperature of an operational greenhouse in situ. In particular, the computational/energy trade-off of these platforms is assessed to analyse whether their use in this context is feasible. Two artificial neural networks (ANNs) are adapted to these platforms to predict the indoor temperature of the greenhouse. Our results show that the microcontroller-based devices can offer a competitive and energy-efficient computational alternative to more traditional edge computing approaches for lightweight ML workloads.
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/10952/7385
url http://hdl.handle.net/10952/7385
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
_version_ 1869403332577067008
score 15,198674

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