Author(s): Rojas Santelices, Ignacio ; Cano, Sandra ; Moreira, Fernando ; Peña Fritz, Álvaro
Date: 2025
Origin: Repositório da Universidade Portucalense
Subject(s): Fruit classification; quality inspection; quality control; computer vision; image processing; artificial vision; deep learning; artificial intelligence; Ciências Naturais - Ciências da Computação e da Informação
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.
