Non-destructive Laboratory Analysis of the Detection of Unhealthy Sugarcane Stalks Using a Portable Vis-NIR Spectrometer

Abstract

The production of sugar is adversely affected by unhealthy sugarcane, which decreases the yield and quality and is difficult to detect through non-destructive tests. This study aims to accurately differentiate between healthy and unhealthy sugarcane in a laboratory environment using a portable visible near-infrared spectrometer with multivariate analyses. The spectra of 100 each of healthy and unhealthy sugarcane parts are analyzed in this study. The classification rates of the partial least-squares discriminant analysis and support vector machine classification of healthy models are 100 % and 91.9 %, respectively, while the classification rates of unhealthy models are 65.2 % and 78.3 %, respectively. The overall results demonstrate that NIR spectroscopy and multivariate analyses are effective at classifying healthy and unhealthy sugarcane.