Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest

Part of the Advances in Biochemical Engineering/Biotechnology book series (ABE, volume 161)


Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product’s surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.


Hyperspectral Image processing In-line inspection Postharvest Quality Real-time Spectral imaging 



This work has been partially funded by INIA through research projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.IVIA, Centro de AgroingenieríaMoncadaSpain
  2. 2.Departamento de Ingeniería GráficaUniversitat Politècnica de ValènciaValenciaSpain

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