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The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications

The use of technology in agriculture has been gaining significant attention recently. By employing advanced tools and automation and leveraging the latest advancements in the Internet of Things (IoT) and artificial intelligence (AI), the agricultural sector is witnessing improvements in its crop yie...

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Detalles Bibliográficos
Autores principales: Alzuhair, Ahmed, Alghaihab, Abdullah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385791/
https://www.ncbi.nlm.nih.gov/pubmed/37514557
http://dx.doi.org/10.3390/s23146262
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author Alzuhair, Ahmed
Alghaihab, Abdullah
author_facet Alzuhair, Ahmed
Alghaihab, Abdullah
author_sort Alzuhair, Ahmed
collection PubMed
description The use of technology in agriculture has been gaining significant attention recently. By employing advanced tools and automation and leveraging the latest advancements in the Internet of Things (IoT) and artificial intelligence (AI), the agricultural sector is witnessing improvements in its crop yields and overall efficiency. This paper presents the design and performance analysis of a machine learning (ML) model for agricultural applications involving acoustic sensing. This model is integrated into an efficient Artificial Intelligence of Things (AIoT) platform tailored for agriculture. The model is then used in the design of a communication network architecture and for determining the distribution of the computing load between edge devices and the cloud. The study focuses on the design, analysis, and optimization of AI deployment for reliable classification models in agricultural applications. Both the architectural level and hardware implementation are taken into consideration when designing the radio module and computing unit. Additionally, the study encompasses the design and performance analysis of the hardware used to implement the sensor node specifically developed for sound classification in agricultural applications. The novelty of this work lies in the optimization of the integrated sensor node, which combines the proposed ML model and wireless network, resulting in an agricultural-specific AIoT platform. This co-design enables significant improvements in the performance and efficiency for acoustic and ambient sensing applications.
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spelling pubmed-103857912023-07-30 The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications Alzuhair, Ahmed Alghaihab, Abdullah Sensors (Basel) Article The use of technology in agriculture has been gaining significant attention recently. By employing advanced tools and automation and leveraging the latest advancements in the Internet of Things (IoT) and artificial intelligence (AI), the agricultural sector is witnessing improvements in its crop yields and overall efficiency. This paper presents the design and performance analysis of a machine learning (ML) model for agricultural applications involving acoustic sensing. This model is integrated into an efficient Artificial Intelligence of Things (AIoT) platform tailored for agriculture. The model is then used in the design of a communication network architecture and for determining the distribution of the computing load between edge devices and the cloud. The study focuses on the design, analysis, and optimization of AI deployment for reliable classification models in agricultural applications. Both the architectural level and hardware implementation are taken into consideration when designing the radio module and computing unit. Additionally, the study encompasses the design and performance analysis of the hardware used to implement the sensor node specifically developed for sound classification in agricultural applications. The novelty of this work lies in the optimization of the integrated sensor node, which combines the proposed ML model and wireless network, resulting in an agricultural-specific AIoT platform. This co-design enables significant improvements in the performance and efficiency for acoustic and ambient sensing applications. MDPI 2023-07-10 /pmc/articles/PMC10385791/ /pubmed/37514557 http://dx.doi.org/10.3390/s23146262 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
Alzuhair, Ahmed
Alghaihab, Abdullah
The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications
title The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications
title_full The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications
title_fullStr The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications
title_full_unstemmed The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications
title_short The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications
title_sort design and optimization of an acoustic and ambient sensing aiot platform for agricultural applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385791/
https://www.ncbi.nlm.nih.gov/pubmed/37514557
http://dx.doi.org/10.3390/s23146262
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