Image texture analysis of pellets made of lignocellulosic materials

This experiment aimed to find the relation between image texture features of pellets made of various lignocellulosic materials (wood, wheat straw, hay, giant miscanthus, prairie spartina, and giant knotweed) and their physico-mechanical properties (density, compressive energy, maximum compressive strength, modulus of elasticity). Using the Kruskal-Wallis's test, the effect of materials on these properties was examined. Texture features were derived from the grey-level co-occurrence matrix, grey-level run-length matrix, absolute gradient matrix, autoregressive model, and wavelet decomposition, resulting in 86 features, later reduced to 8 factors via explanatory factor analysis. These factors were used as predictors in regression models for physico-mechanical properties. The models for modulus of elasticity achieved R2adj values of 0.91–0.99 (except for hay and wood), compressive stress models achieved 0.65–0.99 (excluding hay and wood), compressive energy models ranged from 0.60 to 0.97 (excluding hay), and density models ranged from 0.56 to 0.97 (excluding wood). The study confirmed a significant correlation between material type, texture parameters and compression resistance, suggesting this method could monitor pellet quality in production.