Study On The Early Warning System Of Tobacco Main Leaf Diseases

To provide timely warning information and control suggestions for major leaf diseases in tobacco planting areas in Jilin Province,this article studied the growth models,prediction models,and control indicators of tobacco brown spot disease,wildfire disease,and corner spot disease.Based on the research results,a tobacco major leaf disease warning system was established using Python language and My SQL database on a web platform.The main results are as follows:Using the field systematic survey data of brown spot disease,wildfire disease,and corner spot disease in tobacco planting areas of Jilin Province from 2018 to 2022,the optimal disease growth model screening program was compiled using Python language to simulate the changes in the disease index of these three diseases with sequential values at any time.The results showed that the logistic function had the best simulation effect on the temporal dynamics of tobacco brown spot disease,while the cubic function had the best simulation effect on the temporal dynamics of tobacco wildfire and corner spot disease.Based on the growth models of three diseases,the incidence patterns of tobacco brown spot disease are derived.The onset period of tobacco brown spot disease is generally late June,and the development of the disease is relatively slow in early July.The development of the disease is fast in mid July,and the disease develops rapidly from late July to early August.In mid August,the disease tends to stabilize and develop slowly until the harvest period.The development patterns of tobacco wildfire disease and tobacco corner spot disease are close to the same,with the onset period in late May.The disease develops slowly in early June,develops rapidly in mid to late June,and develops rapidly in July.However,as the harvest season approaches,the tobacco wildfire disease gradually stabilizes,while the tobacco corner spot disease continues to develop slowly.Using the disease index data of tobacco brown spot and wildfire in Yanji,Dunhua,Liuhe and Changchun of Jilin Province from 2016 to 2021,Mudanjiang of Heilongjiang Province and Xifeng of Liaoning Province in 2016,and meteorological factor data of the Internet of Things,four prediction models of tobacco brown spot(models 1-4)and four prediction models of tobacco wildfire(models 5-8)were established by using machine learning polynomial regression combined with ridge regression method The mean square error(MSE)and mean absolute error(MAE)were used to evaluate each model,and the model 1,model 2,model 5,and model 6with good performance were selected for model fit test and accuracy test.The average fit was 96.19%,93.75%,96.85%,and 86.92%,respectively.The disease index for mid August 2022 was predicted using key predictive factors from mid May to late June2022,with average accuracy rates of 89.16%,86.55%,89.86%,and 81.73%,respectively.Using the 2022 tobacco corner spot disease index data and Io T meteorological factor data in Da’an City,four tobacco corner spot disease prediction models(Model 9-12)were established using machine learning K-nearest neighbor regression method.Based on the three evaluation indicators,Model 10 and Model 12,which performed well,were selected for model fitting and accuracy tests.The average fitting degrees were 94.30% and 89.08%,respectively.The tobacco corner spot disease index for August 11-17,2022 was predicted using key predictive factors from July 11-17,2022,with average accuracy of 89.20% and 86.48%,respectively.By calculating the allowable loss rate,output loss rate,and economic damage allowable level,SPSS 26.0 software was used to regress and analyze the disease index and output loss rate data of tobacco brown spot disease,wildfire disease,and corner spot disease,and to propose the pesticide control indicators for the disease.The results showed that the disease index of tobacco brown spot disease control index ranged from 1.53 to 4.75,which means that the first control begins when the disease index in the tobacco field approaches or reaches 1.53,the disease index of tobacco wildfire disease control index ranges from 1.38 to 4.32,the first control begins when the disease index in the tobacco field approaches or reaches 1.38,the disease index of tobacco corner spot disease control index ranges from 1.35 to 4.75,and the first control begins when the disease index in the tobacco field approaches or reaches 1.35.After the first prevention and treatment,it is necessary to determine whether a second or third prevention and treatment is necessary based on the development of the disease.The backend was built on the Django platform using Python language and My SQL database,and the front-end page was displayed using the layuimini open-source framework.Combined with the established growth model,prediction model,and pesticide control indicators of tobacco main leaf diseases,a tobacco main leaf disease warning system was implemented.At present,the system database contains meteorological factor data and disease index data of tobacco planting areas in Jilin Province from 2018 to 2022,used for data query and system function module call.Implemented query functions for disease growth models,prediction and warning,and pesticide control suggestions.And completed the testing of the system function,and the overall function is basically achieved.