{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:00:29Z","timestamp":1779382829401,"version":"3.53.1"},"reference-count":0,"publisher":"IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial","issue":"74","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ia"],"abstract":"<jats:p>Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models\u2019 performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieved 97.6% and 94.0% respectively, while in terms of Area Under the Curve (AUC) score, both models achieved more than 99%, with the difference in inference time between each model over 844 images being 13 seconds.<\/jats:p>","DOI":"10.4114\/intartif.vol27iss74pp48-61","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T04:50:24Z","timestamp":1715921424000},"page":"48-61","source":"Crossref","is-referenced-by-count":3,"title":["FRESHNets: Highly Accurate and Efficient Food Freshness Assessment Based on Deep Convolutional Neural Networks"],"prefix":"10.4114","volume":"27","author":[{"given":"Jorge Felix","family":"Mart\u00ednez Pazos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Gul\u00edn Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Batard Lorenzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arturo","family":"Orellana Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2598","published-online":{"date-parts":[[2024,5,17]]},"container-title":["Inteligencia Artificial"],"original-title":[],"link":[{"URL":"http:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/1369\/223","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/1369\/223","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T04:50:30Z","timestamp":1715921430000},"score":1,"resource":{"primary":{"URL":"http:\/\/journal.iberamia.org\/index.php\/intartif\/article\/view\/1369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":0,"journal-issue":{"issue":"74","published-online":{"date-parts":[[2024,5,17]]}},"URL":"https:\/\/doi.org\/10.4114\/intartif.vol27iss74pp48-61","relation":{},"ISSN":["1988-3064","1137-3601"],"issn-type":[{"value":"1988-3064","type":"electronic"},{"value":"1137-3601","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5,17]]}}}