Research And Application Of Anhui Wheat Head Blight Forecasting Based On Support WebGIS

Wheat is the main grain crop in our country,it is easy to be affected by scab in the process of growth,which affects the yield and quality of wheat.The direct and efficient prediction of the occurrence of wheat disease is of great significance to the efficient prevention and control system of wheat disease.In this paper,by using the grey model prediction and BP neural network algorithm,the prediction results of the two models are compared,and a method with higher prediction accuracy is determined.A prediction model for wheat scab in anhui province was designed.The incidence of wheat scab was classified into 4 grades and the occurrence of the disease was predicted.Based on WebGIS platform,Google map is used to display the incidence level of wheat disease,and visualization technology is used to analyze and display the prediction results of wheat disease and analyze the development trend.The use of GIS to achieve regional positioning,easy to visually observe the incidence of the region and level.The BP neural network algorithm is used to analyze the wheat disease data and predict its occurrence probability,so as to facilitate the plant protection department to formulate the corresponding prevention and control plan and ensure the wheat yield and farmers’ increase and income.The main work is as follows:(1)Wheat scab data were pretreated.This paper analyzed the meteorological factors affecting the occurrence of wheat scab.In the database of crop disease and insect pest monitoring and warning platform of anhui province,the average wheat scab panicle rate and meteorological data of major cities in anhui province from 2009 to 2018 were selected,and the selected data were processed and analyzed,laying a data foundation for the experiment in the following paper.(2)a prediction model of Anhui wheat scab based on GM(1,1)was established.The panicle rate data of wheat scab was used as the original sequence of the model and matlab was used to calculate the model.(3)a prediction model of fusarium scab of Anhui wheat based on BP neural network was constructed.In the information display,the meteorological environmental factors of wheat were analyzed.First,the data of previous times were selected as samples and processed by BP neural network algorithm to establish a prediction model,and then other data were predicted.The model will conduct model training according to the selected sample data,use the adaptive ability to automatically adjust until the conditions are met,and then use other data for testing to ensure the reliability.By comparing the final prediction values of the above two prediction models,it can be concluded that the BP neural network algorithm is more accurate,so the latter is finally adopted as the prediction model.(4)Realize the visualization of disease prediction based on WebGIS.Wheat disease was predicted by the prediction model and the results were displayed on the Google map.The severity grade and the area of disease were visually observed by the thermal map,and the loss degree could be estimated on the whole and the visualization effect could be achieved.In order to control the disease area,to control the disease area to expand further.(5)Modular system design.The system adopts modular design and divides the whole system into information display and background management of the front end,and display of the map area of the front end.The predicted disease situation results are compared with the real results,and the occurrence area of disease over the years is compared with the planting area.Background management has data analysis and display and user information management.The design and development of this study provides an efficient technical tool for the prediction and prediction of major wheat diseases,which is an effective way to solve the prediction of wheat diseases by combining information processing technology with agricultural disease control.