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Acoustic dataset of coconut (Cocos nucifera) based on tapping system

During the fruit sample preparation process, coconut fruits classified under the tall coconut variety in their post-harvest period are considered the subject of this article. All samples are pre-classified by local farmers and experts into three maturity levels; premature, mature, and overmature. Ea...

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Detalles Bibliográficos
Autores principales: Caladcad, June Anne, Piedad, Eduardo Jr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926183/
https://www.ncbi.nlm.nih.gov/pubmed/36798598
http://dx.doi.org/10.1016/j.dib.2023.108936
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author Caladcad, June Anne
Piedad, Eduardo Jr
author_facet Caladcad, June Anne
Piedad, Eduardo Jr
author_sort Caladcad, June Anne
collection PubMed
description During the fruit sample preparation process, coconut fruits classified under the tall coconut variety in their post-harvest period are considered the subject of this article. All samples are pre-classified by local farmers and experts into three maturity levels; premature, mature, and overmature. Each coconut underwent the synchronized tapping and recording process using developed hardware and software. The analog recordings are then converted into digital signals. Sampled frequency and amplitude in discrete-time signals of each sample went through a quantization process. The data presented in this article provides the general differentiation of the coconuts according to their maturity levels through their acoustic properties. This dataset can also be useful in creating an advanced and intelligent classification system of fruits through machine learning and deep learning techniques.
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spelling pubmed-99261832023-02-15 Acoustic dataset of coconut (Cocos nucifera) based on tapping system Caladcad, June Anne Piedad, Eduardo Jr Data Brief Data Article During the fruit sample preparation process, coconut fruits classified under the tall coconut variety in their post-harvest period are considered the subject of this article. All samples are pre-classified by local farmers and experts into three maturity levels; premature, mature, and overmature. Each coconut underwent the synchronized tapping and recording process using developed hardware and software. The analog recordings are then converted into digital signals. Sampled frequency and amplitude in discrete-time signals of each sample went through a quantization process. The data presented in this article provides the general differentiation of the coconuts according to their maturity levels through their acoustic properties. This dataset can also be useful in creating an advanced and intelligent classification system of fruits through machine learning and deep learning techniques. Elsevier 2023-01-26 /pmc/articles/PMC9926183/ /pubmed/36798598 http://dx.doi.org/10.1016/j.dib.2023.108936 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Caladcad, June Anne
Piedad, Eduardo Jr
Acoustic dataset of coconut (Cocos nucifera) based on tapping system
title Acoustic dataset of coconut (Cocos nucifera) based on tapping system
title_full Acoustic dataset of coconut (Cocos nucifera) based on tapping system
title_fullStr Acoustic dataset of coconut (Cocos nucifera) based on tapping system
title_full_unstemmed Acoustic dataset of coconut (Cocos nucifera) based on tapping system
title_short Acoustic dataset of coconut (Cocos nucifera) based on tapping system
title_sort acoustic dataset of coconut (cocos nucifera) based on tapping system
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926183/
https://www.ncbi.nlm.nih.gov/pubmed/36798598
http://dx.doi.org/10.1016/j.dib.2023.108936
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