In the context of Industry 4.0, the textile manufacturing sector is transitioning from physical color management to digitalized workflows. However, the accurate digital reproduction of color on knitted substrates remains a persistent engineering challenge due to the complex surface topology of the fabric, which induces anisotropic scattering and shadowing effects. This study proposes a novel Digital Color Consistency Control (DCCC) framework that integrates a texture-compensated Kubelka-Munk model with spectral imaging techniques. Unlike traditional colorimetry which relies on tristimulus values (L*a*b*), this method utilizes full-spectrum reflectance reconstruction (400-700 nm) to isolate pigment absorption from stray light induced by surface geometry. Experimental validation was conducted on 100% combed cotton single jersey fabrics dyed with reactive dyes. The results indicate that the proposed algorithm reduces the mean color difference (∆E00) from 1.18 (conventional method) to 0.48 and significantly lowers the metamerism index under secondary illuminants. By establishing a closed-loop digital standard that accounts for fabric texture, this research offers a robust theoretical and practical solution for minimizing batch-to-batch variations and shortening the lab-dip lead time in globalized supply chains.

Digital Color Management, spectral reflectance reconstruction, quality control, Kubelka-Munk theory, textile engineering