Exploring the effect of various factors for ash content estimation via ensemble learning: Color-texture features, particle size, and magnification

[Display omitted] •An approach is proposed for features contribution analysis to estimate ash content.•The dataset is formed with five particle sizes coal under four magnifications.•Ensemble learning algorithm is used to effectively estimate ash content online.•Color features play a dominant role in ash content estimation.•Using color-texture mixed features can achieve better estimation results. Online estimation of ash content in coal based on machine vision has been paid more attention to by academia and industry. Existing research has mainly focused on feature extraction and model design for estimating ash content, but the exploration of the feature's contribution to the model is rarely reported. Therefore, this paper presented a method to analyze features' contribution to microscopic images. Expressly, ensemble learning could explicitly point out the assistance of different features to ash content estimation according to the results of weak regressors, which possessed good interpretability. Firstly, we explored the relationship among particle size, magnification, and precision to determine Adaboost as the optimal regressor. Secondly, we discussed the contribution of different features to the regressor quantitatively and qualitatively on the 0.5–0.25 (mm) particle size of datasets under magnification of 0.3, which we called the optimal condition. Ultimately, the experimental conclusions were validated by feature importance analysis and correlation analysis. The experimental results show that the magnification significantly influenced the precision of ash content estimation. Still, coal microscopic images' 0.5–0.25 (mm) particle size estimation accuracy was optimal under all magnifications. When magnification was 0.3 and particle size was 0.5–0.25 (mm), there was the best performance to ash content estimation (In the testing set, 98% of the samples had an error less than 0.5% between ash content estimated value and ash content value). Color features played a dominant role in ash content estimation in the optimal regressors. Only the building color feature regressor could achieve that 96.4% of samples had an estimated error of less than 0.5%. In contrast, only 68% of the samples using the texture feature regressors had an estimated error of less than 0.5%. Moreover, the building mixture of color and texture regressor possessed mutually reinforcing effects under specific conditions. The summarized features' contribution was essential for model building and