Fabric and Yarn Color Prediction Based on Random Forest and Weaving Parameters

Texture is a critical factor influencing the color measurement and analysis of textiles. In this study, we use six yarns of different colors to weave fabric samples with different textures by adjusting weaving parameters such as weft density, reed count, and pattern using an SGA598 fully automatic rapier loom. The yarn and fabric color data were measured using an X-Rite Color i7 spectrophotometer. Correlation analysis between fabric weaving parameters and their color data revealed a nonlinear relationship. Therefore, we construct the forward and backward color prediction model for yarn and fabric based on weaving parameters using the Random Forest (RF) algorithm. The model was validated using the k-fold cross-validation and compared with multi-layer perceptron (MLP) and multi-output linear regression (MOLR) models. Results demonstrated that the forward color prediction model (yarn to fabric) achieved a coefficient of determination (R2 ) of 0.9997, with average predicted color differences of ∆Eab and ∆E00 being 0.342 and 0.241. The backward color prediction model (fabric to yarn) achieved an R2 of 0.9999, with average predicted color differences of ∆Eab and ∆E00 being 0.166 and 0.115. The RF model’s prediction accuracy significantly outperformed that of the MLP and MOLR models, providing support for color measurement and analysis in textiles.