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

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An Erratum to this article was published on 28 April 2011

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.

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Abbreviations

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

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Acknowledgements

This work was supported by the Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria de España (INIA) through research project RTA2009-00118-C02-01 and by the Ministerio de Ciencia e Innovación de España (MICINN) through research project DPI2010-19457, both projects with the support of European FEDER funds.

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Correspondence to Jose Blasco.

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An erratum to this article can be found at http://dx.doi.org/10.1007/s11947-011-0585-8

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Cubero, S., Aleixos, N., Moltó, E. et al. Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables. Food Bioprocess Technol 4, 487–504 (2011). https://doi.org/10.1007/s11947-010-0411-8

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