Automated Classification of Peach Tree Rootstocks by Means of Spectroscopic Measurements and Signal Processing Techniques

Abstract

A new frontier of nondestructive measurement exploitation is explored in this paper as concerns the food traceability and control arena. Presented is a method for nondestructive automated peach tree rootstock classification by means of high spectral resolution spectroscopy and multivariate signal processing. Many studies have shown that rootstock has significant impact on the quality and maturity of peach fruits. Rootstock knowledge not only enables fruit traceability but also helps in selecting the best storage condition and marketing channel. A novel automated method is presented to classify peach fruits with respect to their rootstock based on multivariate signal processing of fruit skin reflectance spectra, which are highly affected by physical and biochemical phenomena associated with fruit quality and maturity. The experimental results exploiting measurements of fruit skin reflectance spectra acquired in the visible and near infrared ranges with a high-resolution spectrometer show that automated rootstock classification by spectroscopic measurements and signal processing techniques is feasible and effective and has great potential in horticultural engineering.