Prediction of environmental parameters and early warning of cherry cracking in greenhouse by GA-Attention-LSTM algorithm
Aiming at the influence of greenhouse environmental factors on cherries,a set of automatic environmental monitoring device for large cherry greenhouse was designed to collect environmental parameter values in the greenhouse to provide digital early warning support and control plan for cherry cracked fruit.Based on the environmental parameter values,the correlation analysis was used to obtain the environmental parameter characteristics with strong correlation with the cracked fruit in the shed.Secondly,the sliding window method was used to generate the input environment features into a time series matrix form.Then,a prediction model integrating the GA-Attention-LSTM algorithm was proposed to accurately predict the environmental parameters in the shed.Finally,SPSS data analysis software was used to analyze the environmental parameters and fruit splitting rate of different greenhouses.The average absolute error of the proposed prediction model with GA-Attention-LSTM algorithm was 0.112 and the mean squared error was 0.087,which was 12.80%and 9.72%higher than that of the LSTM network model,and the prediction accuracy of environmental parameters was higher,and a set of scientific cherry environmental parameter value ranges was obtained,which could provide strong support for the prediction model for the digital early warning of cherry split fruit.
万方数据
维普
