Maize sheath blight prediction based on GC-Forest algorithm
Crop safety is an important factor to achieve high yield.In recent years,machine learning algorithms have provided new ideas for the prediction of maize leaf bacterial wilt.Based on the study of maize leaf bacterial wilt and machine learning algorithms,aiming at the shortcomings of complex model and poor performance of traditional machine learning algorithms,this paper proposed a multi-granularity cascade forest algorithm to realize the prediction of maize leaf bacterial wilt.The max-min standardization and Z-Score standardization methods are used to preprocess the data,and the univariate test and Pearson coeffi-cient are used to measure the selection of feature parameters.Then the selected feature parameters are used as the input variables of the prediction model to establish a multi-granularity cascade forest predic-tion model,and the model is run on the test set.The root mean square error,average relative error and de-termination coefficient were used to evaluate the performance of the model.The results show that the de-termination coefficient of multi-granularity cascade forest model is significantly higher than that of BP neural network and random forest algorithm,and it has better prediction effect.Crop safety is an impor-tant factor to achieve high yield.In recent years,machine learning algorithms have provided new ideas for the prediction of maize leaf bacterial wilt.Based on the study of maize leaf bacterial wilt and machine learning algorithms,aiming at the shortcomings of complex model and poor performance of traditional machine learning algorithms,this paper proposed a multi-granularity cascade forest algorithm to realize the prediction of maize leaf bacterial wilt.The max-min standardization and Z-Score standardization meth-ods are used to preprocess the data,and the univariate test and Pearson coefficient are used to measure the selection of feature parameters.Then the selected feature parameters are used as the input variables of the prediction model to establish a multi-granularity cascade forest prediction model,and the model is run on the test set.The root mean square error,average relative error and determination coefficient were used to evaluate the performance of the model.The results show that the determination coefficient of multi-granu-larity cascade forest model is significantly higher than that of BP neural network and random forest algo-rithm,and it has better prediction effect.
NETL
NSTL
万方数据
