Food and Bioprocess Technology

, Volume 4, Issue 4, pp 487–504

Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables

Authors

  • Sergio Cubero
    • Centro de AgroingenieríaInstituto Valenciano de Investigaciones Agrarias
  • Nuria Aleixos
    • Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser HumanoUniversidad Politécnica de Valencia
  • Enrique Moltó
    • Centro de AgroingenieríaInstituto Valenciano de Investigaciones Agrarias
  • Juan Gómez-Sanchis
    • Intelligent Data Analysis Laboratory (IDAL), Electronic Engineering DepartmentUniversitat de València
    • Centro de AgroingenieríaInstituto Valenciano de Investigaciones Agrarias
Review Paper

DOI: 10.1007/s11947-010-0411-8

Cite this article as:
Cubero, S., Aleixos, N., Moltó, E. et al. Food Bioprocess Technol (2011) 4: 487. doi:10.1007/s11947-010-0411-8

Abstract

Artificial vision systems are powerful tools for the automatic inspection of fruits and vegetables. Typical target applications of such systems include grading, quality estimation from external parameters or internal features, monitoring of fruit processes during storage or evaluation of experimental treatments. The capabilities of an artificial vision system go beyond the limited human capacity to evaluate long-term processes objectively or to appreciate events that take place outside the visible electromagnetic spectrum. Use of the ultraviolet or near-infrared spectra makes it possible to explore defects or features that the human eye is unable to see. Hyperspectral systems provide information about individual components or damage that can be perceived only at particular wavelengths and can be used as a tool to develop new computer vision systems adapted to particular objectives. In-line grading systems allow huge amounts of fruit or vegetables to be inspected individually and provide statistics about the batch. In general, artificial systems not only substitute human inspection but also improve on its capabilities. This work presents the latest developments in the application of this technology to the inspection of the internal and external quality of fruits and vegetables.

Keywords

Computer visionImage analysisFruits and vegetablesAutomatic inspectionInternal qualityHyperspectralIn-line grading

Nomenclature

ANN

Artificial neural networks

ANOVA

Analysis of variance

BMP

Bitmap image format

CA

Correlation analysis

CART

Classification and regression trees

CCD

Charge-coupled device

CMOS

Complementary metal oxide semiconductor

CNN

Competitive neural networks

CT

Computed tomography

DA

Discriminant analysis

GALDA

Genetic algorithm based on LDA

HSI

Hue, saturation, intensity colour space

HSV

Hue, saturation, value colour space

JPG

Joint Photographic Experts Group image format

k-NN

k-nearest neighbour

L*a*b*

CIE-Lab colour space

LDA

Linear discriminant analysis

Luv

CIE-Luv colour space

MI

Mutual information

MIA

Multivariate image analysis

MRI

Magnetic resonance imaging

NIR

Near-infrared

PCA

Principal component analysis

PCI

Peripheral component interconnect

PLS

Partial least square

RGB

Red, green, blue colour space

sRGB

Standard RGB

SSC

Soluble solids content

SVM

Support vector machine

SW

Stepwise multivariate analysis

TA

Titratable acid

TIF

Tagged image file format

USB

Universal serial bus

UV

Ultraviolet

UVFL

Ultraviolet-induced fluorescence

XYZ

XYZ colour space

Copyright information

© Springer Science + Business Media, LLC 2010