Prediction of spring and summer maize yield in China based on feature analysis and hybrid DHKELM algorithms

Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development, both of which are crucial for food production and security. To develop simple yet highly accurate crop yield prediction models, this study proposed a spring- and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine (DHKELM) algorithm. In this study, four tree-based feature importance analysis algorithms, including classification and regression tree, gradient boosting decision tree, random forest, and extreme gradient boosting algorithms, were utilized to analyze the importance of the factors affecting the yield of spring and summer maize. Then, based on the analysis of the four algorithms, different combinations of factors were established to obtain the optimal combination of features. Moreover, to improve the prediction accuracy of the machine learning model, this study utilized three optimization algorithms, including the bald eagle search algorithm, chaos game optimization (CGO) algorithm, and carnivorous plant algorithm, to optimize the hyperparameters in the DHKELM algorithm. The results of the study showed that planting density and plant height were important factors affecting maize yield, and net solar radiation (R_n) received during the reproductive period exhibited the highest relative importance. Appropriate feature combinations can effectively improve model prediction accuracy. The optimal feature combination for spring maize included planting density, plant height, R_n, mean temperature (T_mean), minimum temperature (T_min), and cumulative temperature, and the optimal feature combination for summer maize included R_n, plant height, planting density, T_min, and T_mean. Among the three optimization algorithms, the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm. When the optimal combination of features was used as input, the CGO–DHKELM model used for maize yield prediction provided the following values: RMSE=1.488 t/hm², R²=0.862, MAE=1.051 t/hm², and NSE=0.852 for spring maize; RMSE=1.498 t/hm², R²=0.892, MAE=1.055 t/hm², and NSE=0.891 for summer maize. Thus, the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.