Construction and Application of a Tobacco Target Spot Disease Prediction Model and Digital Platform
In order to accurately predict the dynamics of tobacco target spot disease in the field,a disease index prediction system was established.SPSS Statistics 26,R4.2 and JAVA-SE were used to analyze the factors such as temperature,humidity and rainfall affecting the incidence of target spot disease in tobacco production bases in the field.The results show that there is a strong correlation between the meteorological factors of 10-day average temperature(X1),10-day average humidity(X2),and 10-day average daily maximum temperature(X4)with the to-bacco target spot disease index.The grey correlation between environmental factors and disease index varied at different time periods.A multiple regression prediction model based on the dis-ease index and appeal factors:Y=-50.454+0.414X1+0.184X2+1.313X4 was established.The model was validated in three selected validation sites in the tobacco growing area of Tiansu-an,Yaozhan,Longtang in Wushan,Chongqing,with an accuracy rate of 88.8％.Based on the J2EE platform,the Spring application framework was integrated to develop a tobacco target spot disease index prediction system.The prediction model was injected into the user application component,and an interactive interface was generated after analysis,including on-site disease warning,disease severity judgment,prevention and control measures and suggestions.This study provides important reference for the digitization and intelligent warning and control of to-bacco diseases.
tobacco target spotecological factorsgrey correlation degreeforecast modelpre-diction system
万方数据
维普
