Predicting cotton fiber properties from fiber length parameters measured by dual-beard fibrograph

Cotton fiber properties, although strongly influenced by plant growth conditions, are largely dictated by the cotton variety; therefore, certain inherent associations exist among these properties. Previous studies examined the mutual influences of cotton properties (e.g., fiber maturity on strength), but latent associations between fiber length and other important properties (e.g., fineness, maturity and strength) have not been explored. This paper attempted to investigate these relationships, and to create regression models to predict the fiber properties from the length parameters so that an overview on cotton quality can be provided when only length measurements are available. We collected 100 cotton samples as a training set and 17 extra samples as a testing set, and measured the fiber length parameters using the dual beard fibrograph and the seven other fiber properties (strength, elongation, micronaire, nep, fineness, immature fiber content, and maturity ratio) using the High Volume Instrument and Advanced Fiber Information System. We then performed the correlations, multicollinearity, regression and clustering analyses on the fiber properties. It was found that the fiber length parameters had moderate associations (0.3<| r |<0.7) with the seven properties, and the prediction errors for the training set varied from 2.25% (maturity ratio) to 14.36% (nep). The Bland–Altman analysis proved that for all the seven properties, more than 94.9% of the predicted and actual points were within the 95% agreement limits and without systematic biases. The regression models based on the five cotton clusters consistently lowered the prediction errors through the optimally aggregated fiber properties. The comparable results were obtained from the testing set, which demonstrated the good generalization power of the prediction models.