D
D. Lorente
Researcher at University of Valencia
Publications - 17
Citations - 1148
D. Lorente is an academic researcher from University of Valencia. The author has contributed to research in topics: Hyperspectral imaging & Random forest. The author has an hindex of 11, co-authored 17 publications receiving 948 citations.
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Journal ArticleDOI
Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment
D. Lorente,Nuria Aleixos,Juan Gómez-Sanchis,Sergio Cubero,Oscar Leonardo García-Navarrete,José Blasco +5 more
TL;DR: The different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products are explained, with details of the statistical techniques most commonly used for this task.
Journal ArticleDOI
Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks
TL;DR: This work proposes a methodology to select features in multi-class classification problems using the receiver operating characteristic curve, in order to detect rottenness in citrus fruits by means of hyperspectral images.
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Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning
Nayeli Vélez Rivera,Juan Gómez-Sanchis,Jorge Chanona-Pérez,Juan J Carrasco,Mónica Millán-Giraldo,Mónica Millán-Giraldo,D. Lorente,Sergio Cubero,José Blasco +8 more
TL;DR: In this article, the pericarp of mangos at different stages of ripeness was evaluated based on the analysis of hyperspectral images, and a 97.9% rate was achieved on the third day after the damage had been caused using k-Nearest Neighbours and the whole spectra.
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A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time
F. Martínez-Martínez,Maria J. Ruperez-Moreno,Marcelino Martínez-Sober,J. A. Solves-Llorens,D. Lorente,Antonio J. Serrano-López,S. Martínez-Sanchis,Carlos Monserrat,José D. Martín-Guerrero +8 more
TL;DR: A data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms.
Journal ArticleDOI
Visible–NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit
TL;DR: In this article, the feasibility of reflectance spectroscopy in the visible and near infrared (NIR) regions was evaluated for the automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit.