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

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Measurement, Modeling and Automation in Advanced Food Processing

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

Abstract

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.

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Acknowledgement

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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Authors and Affiliations

  1. IVIA, Centro de Agroingeniería, Cra. Moncada-Náquera km 5, 46113, Moncada, Spain

    José Blasco, Sandra Munera, Sergio Cubero & Enrique Molto

  2. Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain

    Nuria Aleixos

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  1. José Blasco
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  2. Sandra Munera
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  3. Nuria Aleixos
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Correspondence to Enrique Molto .

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  1. Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Baden-Württemberg, Germany

    Bernd Hitzmann

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Blasco, J., Munera, S., Aleixos, N., Cubero, S., Molto, E. (2017). Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest. In: Hitzmann, B. (eds) Measurement, Modeling and Automation in Advanced Food Processing. Advances in Biochemical Engineering/Biotechnology, vol 161. Springer, Cham. https://doi.org/10.1007/10_2016_51

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