Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning
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AutorVelez Rivera, Nayeli; Gomez-Sanchis, Juan; Chanona-Perez, Jorge; Jose Carrasco, Juan; Millan-Giralolo, Monica; Lorente, Delia; Cubero, Sergio; Blasco, José
Cita bibliográficaVelez Rivera, Nayeli, Gomez-Sanchis, J., Chanona-Perez, J., J. Carrasco, J., Millan-Giralolo, Monica, Lorente, Delia, Cubero, Sergio, Blasco, J. (2014). Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosystems Engineering, 122, 91-98.
Mango fruit are sensitive and can easily develop brown spots after suffering mechanical stress during postharvest handling, transport and marketing. The manual inspection of this fruit used today cannot detect the damage in very early stages of maturity and to date no automatic tool capable of such detection has been developed, since current systems based on machine vision only detect very visible damage. The application of hyperspectral imaging to the postharvest quality inspection of fruit is relatively recent and research is still underway to find a method of estimating internal properties or detecting invisible damage. This work describes a new system to evaluate mechanically induced damage in the pericarp of 'Manila' mangos at different stages of ripeness based on the analysis of hyperspectral images. Images of damaged and intact areas of mangos were acquired in the range 650-1100 nm using a hyperspectral computer vision system and then analysed to select the most discriminating wavelengths for distinguishing and classifying the two zones. Eleven feature-selection methods were used and compared to determine the wavelengths, while another five classification methods were used to segment the resulting multispectral images and classify the skin of the mangos as sound or damaged. A 97.9% rate of correct classification of pixels was achieved on the third day after the damage had been caused using k-Nearest Neighbours and the whole spectra and the figure dropped to 91.4% when only the most discriminant bands were used. (C) 2014 IAgrE. Published by Elsevier Ltd. All rights reserved.