Prediction of bran speck content in wheat flour based on XGBoost with Bayesian optimization

Aiming at the problems of segmentation difficulty and significant statistical errors in determining wheat flour bran speck content using traditional image processing methods, a predictive model for wheat flour bran speck content based on Bayesian-optimized eXtreme Gradient Boosting (XGBoost) algorithm was proposed. The Variance Inflation Factor (VIF) was utilized to perform feature selection on the color and texture characteristics of wheat flour RGB image, thereby constructing a dataset for wheat flour bran speck content. The selected features and bran speck content parameters were used as input and data labels of the model respectively. After training, comparative experiments are conducted, with random Forest (RF), Gradient Boosting Decision Tree (GBDT), AdaBoost (Adaptive Boosting), and Convolutional Neural Network (CNN). Experimental results indicated that the mean absolute error of XGBoost is 0.004 4, which represented a reduction in mean absolute error by 18.51%, 22.81%, 18.51% and 24.14% compared with RF, GBDT, AdaBoost, and CNN, respectively. This algorithm model can accurately predict the bran speck content, offering significant practical value for guiding the moderate processing of wheat flour and enhancing detection efficiency.