Machine learning and experimental testing for prediction of breakage rate of maize kernels based on components contents

A high breakage rate (BR) of maize kernels is the main problem during the direct harvest of maize kernels which causes massive grain losses. Thus, a solution was provided for reducing BR in harvest by predicting BR to select a suitable harvest time. The BR prediction models of maize kernels based on moisture, protein and starch contents were studied by using multivariate polynomial regression, stepwise polynomial regression, support vector regression (SVR) and extreme learning regression. SVR with radial basis function (rbf-SVR) was selected for further analysis. The performances of 7 different rbf-SVR models with single and multiple combinations of three components contents were evaluated. The rbf-SVR model constructed with moisture, protein and starch contents (rbf-SVR Ms + Pr + St ), which were regarded as predictor variables, generated the most accurate BR estimate. The correlation coefficients of the correction set and prediction set were 0.8921 and 0.8776, respectively. The root mean square errors of the correction set and prediction set were 1.3898% and 1.3767%, respectively. The adjusted R 2 was 0.7851. The average classification accuracy was 82.17%. As a result, the rbf-SVR Ms + Pr + St model can comprehensively evaluate BR and guide the selection of appropriate harvest time, to reduce the BR. • The relationships between components contents and BR of maize kernels were studied. • The BR prediction models were built by machine learning (MPR, SPR, SVR and ELR). • The rbf-SVR Ms + Pr + St model achieved the highest accuracy in predicting BR. • A solution was provided for reducing BR in harvest.