Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system

Abstract

Specific amounts of solid impurities in raw sugarcane need to be detected before raw materials are carried into mills. Solid impurities come from the plant, e.g., green and dry leaves and soil. This study proposed to classify sugarcane via a new strategy using a well-established method that combines digital images converted into ten color-scale color histograms of red (R), green (G) and blue (B), RGB; hue (H), saturation (S) and value (v), HSV; relative colors of RGB, rgb; and luminosity (L) with multivariate classification methods. Sampling was performed using a mixture design that comprised 122 different combinations of sugarcane stalks, vegetal plant parts and soil to achieve 100 wt% for evaluating the desirable and undesirable situations for the solid impurity amounts. Classical algorithms, such as soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and k nearest neighbors (kNN), were used to perform the calculations. Receive operating characteristic (ROC) revealed the high sensitivity and specificity of the three algorithms using the color histogram data. The outstanding result was the ability to classify sugarcane content higher than 85 wt%, which is considered high-quality raw material by cane mills.