{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:11:36Z","timestamp":1777590696761,"version":"3.51.4"},"reference-count":81,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, I.P.","award":["UIDB\/05105\/2020"],"award-info":[{"award-number":["UIDB\/05105\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fruit sorting and quality inspection using computer vision is a key tool to ensure quality and safety in the fruit industry. This study presents a systematic literature review, following the PRISMA methodology, with the aim of identifying different fields of application, typical hardware configurations, and the techniques and algorithms used for fruit sorting. In this study, 56 articles published between 2015 and 2024 were analyzed, selected from relevant databases such as Web of Science and Scopus. The results indicate that the main fields of application include orchards, industrial processing lines, and final consumption points, such as supermarkets and homes, each with specific technical requirements. Regarding hardware, RGB cameras and LED lighting systems predominate in controlled applications, although multispectral cameras are also important in complex applications such as foreign material detection. Processing techniques include traditional algorithms such as Otsu and Sobel for segmentation and deep learning models such as ResNet and VGG, often optimized with transfer learning for classification. This systematic review could provide a basic guide for the development of fruit quality inspection and classification systems in different environments.<\/jats:p>","DOI":"10.3390\/s25051524","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T03:22:44Z","timestamp":1740972164000},"page":"1524","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Artificial Vision Systems for Fruit Inspection and Classification: Systematic Literature Review"],"prefix":"10.3390","volume":"25","author":[{"given":"Ignacio","family":"Rojas Santelices","sequence":"first","affiliation":[{"name":"Doctorate in Smart Industry, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Avenida Brasil 2141, Valparaiso 2370688, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9583-8532","authenticated-orcid":false,"given":"Sandra","family":"Cano","sequence":"additional","affiliation":[{"name":"School of Informatics Engineering, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Avenida Brasil 2241, Valparaiso 2370688, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0816-1445","authenticated-orcid":false,"given":"Fernando","family":"Moreira","sequence":"additional","affiliation":[{"name":"REMIT (Research on Economics, Management and Information Technologies), IJP (Instituto Jur\u00eddico Portucalense), Universidade Portucalense, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 541-619, 4200-072 Porto, Portugal"},{"name":"IEETA (Instituto de Engenharia Electr\u00f3nica e Telem\u00e1tica de Aveiro), Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2018-1972","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Pe\u00f1a Fritz","sequence":"additional","affiliation":[{"name":"School of Construction and Transportation Engineering, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Avenida Brasil 2147, Valparaiso 2370688, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"ref_1","unstructured":"Economic Research Service U.S. (2024, September 27). 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