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
维普
万方数据
