Estimating Cotton Fiber Biological Fineness via High-Throughput Phenotyping

Biological fineness of cotton fibers significantly affects spinning performance and yarn quality, yet reliable high-throughput measurement is hindered by morphological variability and limitations of conventional cross-sectional methods. This study developed an automated microscopic imaging system for rapid measurement of cotton fiber phenotypic features from longitudinal-view images, eliminating the need for fiber cross-sectioning. The system integrated a light microscope equipped with a three-axis motorized stage and a high-resolution digital camera for automated scanning and image acquisition. At each (x, y) location, a z-stack of images is captured, and in-focus pixels are fused into a single extended depth-of-field (EDF) image. The EDF images are segmented using Meta’s Segment Anything Model, with prompt points automatically generated from fiber skeletons to separate overlapping fibers. The segmented fiber ribbons are scanned transversely along their centerlines to extract ribbon width, image intensity, and intensity variability, which are incorporated into an empirical model to estimate the fiber perimeter (P). Individual measurements from a slide are aggregated to characterize the distribution of P for each cotton sample. The proposed method demonstrated high repeatability across multiple replicates and independent tests. The estimated P showed strong correlations with AFIS gravimetric fineness (r ≈ 0.80) and with HVI bundle strength (r ≈ −0.95), and a moderate correlation with HVI micronaire (r ≈ 0.65). A preliminary validation test against the cross-sectional method suggested that the perimeter distributions obtained from the estimated and cross-sectional measurements did not differ statistically and were highly correlated (r > 0.960).