Multispectral Vision for Monitoring Peach Ripeness

Herrero Langreo, Ana and Lunadei, Loredana and Lleó García, Lourdes and Diezma Iglesias, Belen and Ruiz-Altisent, Margarita (2011). Multispectral Vision for Monitoring Peach Ripeness. "Journal of Food Science", v. 76 (n. 2); pp. 178-187. ISSN 0022-1147. https://doi.org/10.1111/j.1750-3841.2010.02000.x.

Description

Title: Multispectral Vision for Monitoring Peach Ripeness
Author/s:
  • Herrero Langreo, Ana
  • Lunadei, Loredana
  • Lleó García, Lourdes
  • Diezma Iglesias, Belen
  • Ruiz-Altisent, Margarita
Item Type: Article
Título de Revista/Publicación: Journal of Food Science
Date: March 2011
ISSN: 0022-1147
Volume: 76
Subjects:
Freetext Keywords: computer vision;firmness;fruit;postharvest;ripeness.
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Ingeniería Rural [hasta 2014]
UPM's Research Group: LPF-TAGRALIA
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview

Abstract

The main objective of this research was to develop an automatic procedure able to classify Rich Lady commercial peaches according to their ripeness stage through multispectral imaging techniques. A classification procedure was applied to the ratio images calculated as red (R, 680 nm) divided by infrared (IR, 800 nm), that is, R/IR images. Four image-based ripeness reference classes (A: unripe to D: overripe) were generated from 380 fruit images (season 1: 2006) by a nonsupervised classification method and evaluated according to reference measurements of the ripeness of the same samples: Magness-Taylor penetrometry firmness, low-mass impact firmness, reflectance at 680 nm (R680, and soluble solids content. The assignment of unknown sample images from those season 1 images (internal validation, n = 380) and of 240 images from the 2nd season (season 2: 2007) to the ripeness reference classes (external validation) was carried out by computing the minimum Euclidean distance (classification distance, Cd) between each unknown image histogram and the average histogram of each ripeness reference class. For both validation phases, firmness values decreased and R680 increased for increasing alphabetical order of image-based class letter, reflecting the ripening process. Moreover, 70% (season 1) and 80% (season 2) of the samples below bruise susceptibility firmness were classified into class D.

More information

Item ID: 6424
DC Identifier: http://oa.upm.es/6424/
OAI Identifier: oai:oa.upm.es:6424
DOI: 10.1111/j.1750-3841.2010.02000.x
Official URL: http://onlinelibrary.wiley.com/doi/10.1111/j.1750-3841.2010.02000.x/abstract
Deposited by: Memoria Investigacion
Deposited on: 18 Mar 2011 11:43
Last Modified: 20 Apr 2016 15:43
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM