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Food and Bioprocess Technology

, Volume 9, Issue 10, pp 1623–1639 | Cite as

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

  • Sergio Cubero
  • Won Suk Lee
  • Nuria Aleixos
  • Francisco Albert
  • Jose Blasco
Review

Abstract

Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing the detection of not only defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.

Keywords

Citrus sorting Quality inspection Hyperspectral imaging Citrus colour index Citrus canker Citrus decay Citrus Huanglongbing Citrus postharvest 

Notes

Acknowledgments

This work was supported by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) through projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors would like to thank and acknowledge the contributions that were made by all the students, postdocs, technicians and visiting scholars in the Precision Agriculture Laboratory at the University of Florida and the Computer Vision Laboratory at the Agricultural Engineering Centre of IVIA.

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© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Centro de AgroingenieríaInstituto Valenciano de Investigaciones Agrarias (IVIA)MoncadaSpain
  2. 2.Agricultural and Biological Engineering DepartmentUniversity of FloridaFloridaUSA
  3. 3.Departamento de Ingeniería GráficaUniversitat Politècnica de ValènciaValenciaSpain

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