Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.13/5557 |
Resumo: | The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued. |
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Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic ReviewBananaComputer imagingDeep learningMachine learningRipeness.Faculdade de Ciências Exatas e da EngenhariaThe ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.MDPIDigitUMaBaglat, PreetyHayat, AhatshamMendonça, FábioGupta, AnkitMostafa, Sheikh ShanawazDias, Fernando Morgado2024-02-16T10:23:40Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.13/5557engBaglat, P.; Hayat, A.; Mendonça, F.; Gupta, A.; Mostafa, S.S.; Morgado-Dias, F. Non Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review. Sensors 2023, 23, 738. https:// doi.org/10.3390/s2302073810.3390/s23020738info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-18T05:33:24Zoai:digituma.uma.pt:10400.13/5557Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:38:50.192880Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review |
title |
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review |
spellingShingle |
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review Baglat, Preety Banana Computer imaging Deep learning Machine learning Ripeness . Faculdade de Ciências Exatas e da Engenharia |
title_short |
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review |
title_full |
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review |
title_fullStr |
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review |
title_full_unstemmed |
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review |
title_sort |
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review |
author |
Baglat, Preety |
author_facet |
Baglat, Preety Hayat, Ahatsham Mendonça, Fábio Gupta, Ankit Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
author_role |
author |
author2 |
Hayat, Ahatsham Mendonça, Fábio Gupta, Ankit Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
DigitUMa |
dc.contributor.author.fl_str_mv |
Baglat, Preety Hayat, Ahatsham Mendonça, Fábio Gupta, Ankit Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
dc.subject.por.fl_str_mv |
Banana Computer imaging Deep learning Machine learning Ripeness . Faculdade de Ciências Exatas e da Engenharia |
topic |
Banana Computer imaging Deep learning Machine learning Ripeness . Faculdade de Ciências Exatas e da Engenharia |
description |
The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z 2024-02-16T10:23:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.13/5557 |
url |
http://hdl.handle.net/10400.13/5557 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Baglat, P.; Hayat, A.; Mendonça, F.; Gupta, A.; Mostafa, S.S.; Morgado-Dias, F. Non Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review. Sensors 2023, 23, 738. https:// doi.org/10.3390/s23020738 10.3390/s23020738 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
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MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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