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Machine learning system for commercial banana harvesting

2024-07-08Journal article Open access
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datacite.subject.fosCiências Agrárias::Agricultura, Silvicultura e Pescas
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorHayat, Ahatsham
dc.contributor.authorBaglat, Preety
dc.contributor.authorMendonça, Fábio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.contributor.authorBaglat, Preety
dc.contributor.authorSilva Mendonça, Fábio Rúben
dc.contributor.authorMorgado-Dias, Fernando
dc.date.accessioned2025-05-09T12:47:48Z
dc.date.available2025-05-09T12:47:48Z
dc.date.issued2024-07-08
dc.description.abstract<jats:title>Abstract</jats:title> <jats:p>The conventional process of visual detection and manual harvesting of the banana bunch has been a known problem faced by the agricultural industry. It is a laborious activity associated with inconsistency in the inspection and grading process, leading to post-harvest losses. Automated fruit harvesting using computer vision empowered by deep learning could significantly impact the visual inspection process domains, allowing consistent harvesting and grading. To achieve the goal of the industry-level harvesting process, this work collects data from professional harvesters from the industry. It investigates six state-of-the-art architectures to find the best solution. 2,685 samples were collected from four different sites with expert opinions from industry harvesters to cut (or harvest) and keep (or not harvest) the banana brunch. Comparative results showed that the DenseNet121 architecture outperformed the other examined architectures, reaching a precision, recall, F1 score, accuracy, and specificity of 85%, 82%, 82%, 83%, and 83%, respectively. In addition, an understanding of the underlying black box nature of the solution was visualized and found adequate. This visual interpretation of the model supports human expert’s criteria for harvesting. This system can assist or replace human experts in the field.</jats:p>eng
dc.identifier.citationHayat, A., Baglat, P., Mendonça, F., Mostafa, S. S., & Dias, F. M. (2024). Machine learning system for commercial banana harvesting. Engineering Research Express, 6(3), 035202.
dc.identifier.doi10.1088/2631-8695/ad5cd2
dc.identifier.issn2631-8695
dc.identifier.urihttp://hdl.handle.net/10400.13/7266
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIOP Publishing
dc.relation10.54499/LA/P/0083/2020
dc.relation10.54499/UIDP/50009/2020
dc.relation10.54499/UIDB/50009/2020
dc.relation.ispartofEngineering Research Express
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBanana bunch harvesting
dc.subjectDeep learning
dc.subjectComputer vision
dc.subjectAgriculture industry
dc.subject.
dc.subjectFaculdade de Ciências Exatas e da Engenharia
dc.titleMachine learning system for commercial banana harvestingeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue3
oaire.citation.titleEngineering Research Express
oaire.citation.volume6
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBaglat
person.familyNameSilva Mendonça
person.familyNameMorgado-Dias
person.givenNamePreety
person.givenNameFábio Rúben
person.givenNameFernando
person.identifier2616642
person.identifier.ciencia-id1711-4988-4E41
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-3348-262X
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0001-7334-3993
relation.isAuthorOfPublication82529790-b282-4dca-815a-8de72fcc03e6
relation.isAuthorOfPublication0dbad4bf-3a0c-4c43-8022-42199a5e09c0
relation.isAuthorOfPublication042f7593-c6ca-4553-8f0e-12ccf17018be
relation.isAuthorOfPublication.latestForDiscovery82529790-b282-4dca-815a-8de72fcc03e6

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