Identification of mechanical damage in the 'Fuji' apple cv. using artificial hyperspectral vision

Abstract

One problem in the post-harvest phase of apples is the mechanical impact damage; its identification prevents quality issues during storage. The objective was to identify the wavelengths at which the damage is detected early in apples of the 'Fuji' cultivar, simulating the damage with a controlled stroke and taking hyperspectral images from 400 to 1700 nm. Three experiments were carried out at different temperatures (4 and 20 ° C) and with varying sampling times. It was found that in the NIR zone ranging between 1050 and 1100 nm, it was possible to classify healthy and bruised zones by means of a discriminant analysis by partial least squares (PLS-DA). Additionally, the evolution of the damage over time was not significant for the classification of the pixels (healthy and bruised classes), since bumps were detected in all three experiments from the first time.

Keywords

• hyperspectral images
• pls-da
• nir
• spectroscopy