Open AccessArtificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity
Author(s) -
Lucas Jai dos Santos,
Érica Regina Filletti,
Fernando Mendes Pereira
Publication year - 2021
Publication title -
eclética química
Language(s) - English
Resource type - Journals
eISSN - 1678-4618
pISSN - 0100-4670
DOI - 10.26850/1678-4618eqj.v46.3.2021.p49-54
Subject(s) - hue , impurity , raw material , artificial neural network , materials science , mathematics , artificial intelligence , environmental science , biological system , pulp and paper industry , computer science , chemistry , engineering , biology , organic chemistry
An investigation dedicated to evaluating a big issue in biorefineries, solid impurity in raw sugarcane, is presented. This relevant industrial sector requests a high-frequency, low-cost, and noninvasive method. Then, the developed method uses the averaged color values of ten color-scale descriptors: R (red), G (green), B (blue), their relative colors (r, g, and b), H (hue), S (saturation), V (value) and L (luminosity) from digital images acquired from 146 solid mixtures among sugarcane stalks and solid impurity vegetal parts (green and dry leaves) and soil. The solid mixture of samples was prepared considering desirable and undesirable scenarios for the solid impurity amounts. The outstanding result was revealed by an artificial neural network (ANN), achieving 100% of accurate classifications for two ranges of raw sugarcane in the samples: from 90 to 100 wt% and from 41 to 87 wt%. Low-computational cost and a simple setup for image acquisition method could screen solid impurity in sugarcane shipments as a promising application.