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Forecasting Air Temperature on Edge Devices with Embedded AI †
With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s int...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8228015/ https://www.ncbi.nlm.nih.gov/pubmed/34207546 http://dx.doi.org/10.3390/s21123973 |
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author | Codeluppi, Gaia Davoli, Luca Ferrari, Gianluigi |
author_facet | Codeluppi, Gaia Davoli, Luca Ferrari, Gianluigi |
author_sort | Codeluppi, Gaia |
collection | PubMed |
description | With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range [Formula: see text] [Formula: see text] , a Mean Absolute Percentage Error (MAPE) in the range of [Formula: see text] %, and a coefficient of determination (R [Formula: see text]) not smaller than [Formula: see text]. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible. |
format | Online Article Text |
id | pubmed-8228015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82280152021-06-26 Forecasting Air Temperature on Edge Devices with Embedded AI † Codeluppi, Gaia Davoli, Luca Ferrari, Gianluigi Sensors (Basel) Article With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range [Formula: see text] [Formula: see text] , a Mean Absolute Percentage Error (MAPE) in the range of [Formula: see text] %, and a coefficient of determination (R [Formula: see text]) not smaller than [Formula: see text]. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible. MDPI 2021-06-09 /pmc/articles/PMC8228015/ /pubmed/34207546 http://dx.doi.org/10.3390/s21123973 Text en © 2021 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 Codeluppi, Gaia Davoli, Luca Ferrari, Gianluigi Forecasting Air Temperature on Edge Devices with Embedded AI † |
title | Forecasting Air Temperature on Edge Devices with Embedded AI † |
title_full | Forecasting Air Temperature on Edge Devices with Embedded AI † |
title_fullStr | Forecasting Air Temperature on Edge Devices with Embedded AI † |
title_full_unstemmed | Forecasting Air Temperature on Edge Devices with Embedded AI † |
title_short | Forecasting Air Temperature on Edge Devices with Embedded AI † |
title_sort | forecasting air temperature on edge devices with embedded ai † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8228015/ https://www.ncbi.nlm.nih.gov/pubmed/34207546 http://dx.doi.org/10.3390/s21123973 |
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