Repository logo
 
Publication

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

2023Journal article Open access
Simple item page
dc.contributor.authorBaglat, Preety
dc.contributor.authorHayat, Ahatsham
dc.contributor.authorMendonça, Fábio
dc.contributor.authorGupta, Ankit
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2024-02-16T10:23:40Z
dc.date.available2024-02-16T10:23:40Z
dc.date.issued2023
dc.description.abstractThe 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBaglat, 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/s23020738pt_PT
dc.identifier.doi10.3390/s23020738pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5557
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBananapt_PT
dc.subjectComputer imagingpt_PT
dc.subjectDeep learningpt_PT
dc.subjectMachine learningpt_PT
dc.subjectRipenesspt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleNon-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.issue2pt_PT
oaire.citation.startPage738pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume23pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBaglat
person.familyNameSilva Mendonça
person.familyNameGupta
person.familyNameMostafa
person.familyNameMorgado-Dias
person.givenNamePreety
person.givenNameAhatsham
person.givenNameFábio Rúben
person.givenNameAnkit
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.identifier2616642
person.identifier2100796
person.identifier34497
person.identifier.ciencia-id1711-4988-4E41
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-id9111-97C0-B28A
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-3348-262X
person.identifier.orcid0000-0003-2000-5557
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-2310-908X
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id57197874356
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication82529790-b282-4dca-815a-8de72fcc03e6
relation.isAuthorOfPublication1bf77d6f-5a7e-4815-94f6-b4fde83dd7f2
relation.isAuthorOfPublication0dbad4bf-3a0c-4c43-8022-42199a5e09c0
relation.isAuthorOfPublication497d5e03-3736-4e99-a4b4-e0a90957e20d
relation.isAuthorOfPublicationf90aafd0-eedb-47ea-945a-40b1c1fe802a
relation.isAuthorOfPublication042f7593-c6ca-4553-8f0e-12ccf17018be
relation.isAuthorOfPublication.latestForDiscovery497d5e03-3736-4e99-a4b4-e0a90957e20d
relation.isProjectOfPublicationc0352e81-99e6-4923-99ed-074df09e4db0
relation.isProjectOfPublication.latestForDiscoveryc0352e81-99e6-4923-99ed-074df09e4db0

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning.pdf
Size:
1.54 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Artigos em revistas internacionais