Recognition of food ingredients: Dataset analysis

Louro, João; Fidalgo, Filipe; Oliveira, Ângela

Publication

Recognition of food ingredients: Dataset analysis

2024Research article

datacite.subject.sdg	03:Saúde de Qualidade
dc.contributor.author	Louro, João
dc.contributor.author	Fidalgo, Filipe
dc.contributor.author	Oliveira, Ângela
dc.date.accessioned	2025-12-04T16:24:02Z
dc.date.available	2025-12-04T16:24:02Z
dc.date.issued	2024
dc.date.updated	2025-12-02T16:35:09Z
dc.description.abstract	Nowadays, food waste is seen as a complex problem with effects on the social, economic, and environmental domains. Even though this view is widely held, it is frequently believed that individual acts have little to no impact on the issue. But just like with recycling, there may be a significant impact if people start adopting more sustainable eating habits. We suggest using a cutting-edge convolutional neural network (CNN) model to identify food in light of these factors. To improve performance, this model makes use of several strategies, such as fine-tuning and transfer learning. Additionally, we suggest using the Selenium library to create a dataset by employing the web scraping technique. This strategy solves the problem that many open-source datasets have with the overrepresentation of foods from the Asian continent by enabling the addition of foods to the dataset in a customized way. First, using the PRISMA methodology, a thorough examination of recente research in this field will be carried out. We will talk about the shortcomings of the most widely used dataset (Food-101), which prevent the ResNet-50 model from performing well. Using this information, a smartphone app that can identify food and suggest recipes based on the ingredients it finds could be developed. This would prevent food waste that results from the lack of imagination and patience of most people. The food recognition model used was the ResNet-50 convolutional neural network, which achieved 90% accuracy for the validation set and roughly 97% accuracy in training.	eng
dc.description.sponsorship	This work was funded by National Funds through the Foundation for Science and Technology (FCT), I.P., within the scope of the project UIDB/05583/2020 and DOI identifier https://doi.org/10.54499/UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD) and the Instituto Politécnico de Viseu for their support.
dc.description.version	N/A
dc.identifier.citation	LOURO, J. ; FIDALGO, F. ; OLIVEIRA, Â. (2024) - Recognition of food ingredients: Dataset analysis. Appl. Sci. 14, p. 5448. DOI: 10.3390/app14135448
dc.identifier.doi	10.3390/app14135448	en_US
dc.identifier.slug	cv-prod-4112209
dc.identifier.uri	http://hdl.handle.net/10400.11/10385
dc.language.iso	eng
dc.peerreviewed	yes
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Food ingredient recognition
dc.subject	Artificial neural network
dc.subject	Dataset
dc.subject	Web scraping
dc.subject	Recommendation system
dc.subject	ResNet-50
dc.title	Recognition of food ingredients: Dataset analysis	eng
dc.type	research article	en_US
dspace.entity.type	Publication
oaire.citation.title	Applied Sciences	en_US
oaire.citation.volume	14
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyName	Fidalgo
person.familyName	Marques Oliveira
person.givenName	Filipe
person.givenName	Angela Cristina
person.identifier.ciencia-id	BC11-DFB7-A451
person.identifier.ciencia-id	471B-8360-6CF9
person.identifier.orcid	0000-0001-7326-9957
person.identifier.orcid	0000-0003-0172-4679
person.identifier.scopus-author-id	55810696000
rcaap.cv.cienciaid	471B-8360-6CF9 \| Ângela Cristina Marques de Oliveira
rcaap.rights	openAccess	en_US
relation.isAuthorOfPublication	489eda06-3ade-4c15-a54e-fee91030518a
relation.isAuthorOfPublication	743a5c35-45ff-4434-bd00-b2c14691ba19
relation.isAuthorOfPublication.latestForDiscovery	489eda06-3ade-4c15-a54e-fee91030518a