Food and Bioprocess Technology

, Volume 4, Issue 4, pp 487–504 | Cite as

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

  • Sergio Cubero
  • Nuria Aleixos
  • Enrique Moltó
  • Juan Gómez-Sanchis
  • Jose Blasco
Review Paper


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.


Computer vision Image analysis Fruits and vegetables Automatic inspection Internal quality Hyperspectral In-line grading 



Artificial neural networks


Analysis of variance


Bitmap image format


Correlation analysis


Classification and regression trees


Charge-coupled device


Complementary metal oxide semiconductor


Competitive neural networks


Computed tomography


Discriminant analysis


Genetic algorithm based on LDA


Hue, saturation, intensity colour space


Hue, saturation, value colour space


Joint Photographic Experts Group image format


k-nearest neighbour


CIE-Lab colour space


Linear discriminant analysis


CIE-Luv colour space


Mutual information


Multivariate image analysis


Magnetic resonance imaging




Principal component analysis


Peripheral component interconnect


Partial least square


Red, green, blue colour space


Standard RGB


Soluble solids content


Support vector machine


Stepwise multivariate analysis


Titratable acid


Tagged image file format


Universal serial bus




Ultraviolet-induced fluorescence


XYZ colour space



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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Copyright information

© Springer Science + Business Media, LLC 2010

Authors and Affiliations

  • Sergio Cubero
    • 1
  • Nuria Aleixos
    • 2
  • Enrique Moltó
    • 1
  • Juan Gómez-Sanchis
    • 3
  • Jose Blasco
    • 1
  1. 1.Centro de AgroingenieríaInstituto Valenciano de Investigaciones AgrariasMoncadaSpain
  2. 2.Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser HumanoUniversidad Politécnica de ValenciaValenciaSpain
  3. 3.Intelligent Data Analysis Laboratory (IDAL), Electronic Engineering DepartmentUniversitat de ValènciaBurjassot (Valencia)Spain

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