This paper presents an automated approach for peach fruit maturity grading that, by exploiting fiber-optic spectroscopy-based sensors and multivariate processing techniques, minimizes the operator intervention while reducing discharge and waste. The use of a spectroscopic sensor complies with the so-called nondestructive measurement method, which enables fast repeated measurements to be performed at the single fruit level while avoiding fruit damage and loss. Maturity grading is accomplished by retrieving estimates of the fruit flesh firmness by means of multivariate retrieval techniques applied to the reflectance spectra acquired with the spectrometer and by processing the retrieved values within the framework of a maturity fuzzy classifier. A decision support system is developed to provide the user with maturity category decision and associated reliability. Experimental results show that the approach is effective for automated maturity grading of peach fruits affected by a high degree of variability. This paper lays the foundations for the realization of easy-to-use sustainable automated maturity grading systems.

A Spectroscopy-based Approach for Automated Non-Destructive Maturity Grading of Peach Fruits

MATTEOLI, STEFANIA;DIANI, MARCO;MASSAI, ROSSANO;CORSINI, GIOVANNI;REMORINI, DAMIANO
2015-01-01

Abstract

This paper presents an automated approach for peach fruit maturity grading that, by exploiting fiber-optic spectroscopy-based sensors and multivariate processing techniques, minimizes the operator intervention while reducing discharge and waste. The use of a spectroscopic sensor complies with the so-called nondestructive measurement method, which enables fast repeated measurements to be performed at the single fruit level while avoiding fruit damage and loss. Maturity grading is accomplished by retrieving estimates of the fruit flesh firmness by means of multivariate retrieval techniques applied to the reflectance spectra acquired with the spectrometer and by processing the retrieved values within the framework of a maturity fuzzy classifier. A decision support system is developed to provide the user with maturity category decision and associated reliability. Experimental results show that the approach is effective for automated maturity grading of peach fruits affected by a high degree of variability. This paper lays the foundations for the realization of easy-to-use sustainable automated maturity grading systems.
2015
Matteoli, Stefania; Diani, Marco; Massai, Rossano; Corsini, Giovanni; Remorini, Damiano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/752206
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