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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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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DOI: https://doi.org/10.1007/s11947-010-0411-8