Predictive Model For Wheat Leaf Rust In Hebei Plain And Construction Of Intelligent Assessment System

Hebei Plain is one of the main wheat producing areas in China,and it is of great significance to safeguard regional food security.Leaf rust is an important airborne disease in wheat production in Hebei Province.In recent years,its area and degree of damage have increased.At present,in wheat production,leaf rust is mainly controlled by chemical agents in flowering period,and generally depends on the experience and the recommended dose.Therefore,it is comment in production that improper use of drugs leads to poor control efficiency.In addition,the investigation of the occurrence of wheat leaf rust and the disease degree of the leaves mainly rely on manual identification and discrimination,the accuracy and investigation speed are limited,and it cannot meet the needs of large-scale production.Therefore,the accurate prediction in the early stage,and intelligent assess of wheat leaf rust with the information-based means,are of great significance to establish a scientific and efficient prevention and control system of wheat leaf rust which meets the needs of modern agricultural production.In this study,the wheat leaf rust in 12 typical sites in the south,middle and north of Hebei Plain from 1988 to 2016 were selected as the dependent variable.Combining the facts and literature review of wheat leaf rust in different regions,the meteorological elements at key stages were selected as independent variables.Stepwise regression was used to construct a wheat leaf rust prediction model.Using part of unmodeled data from2010 to 2017 in different regions and the measured data from typical sites in 2018,the accuracy of the forecast model was verified.Based on the client/server structure system fusion matlab image recognition technology,rapid intelligent assess of wheat leaf rust was realized through the PC web interface.The main results are as follows:1.The occurrence of wheat leaf rust in Hebei Plain was lighter before 2000 and the growth rate increased slowly.After 2000,the area and degree of damage were both increased significant.Among them,Xingtai and Handan city were more obvious,and their occurrence area and yield loss were both greatly increased.Followed by the central regions represented by Shijiazhuang,Hengshui and Baoding city,and they also belonged to areas where the incidence of leaf rust was aggravated.2.Determine the total rainy days,total rainfall and average temperature in mid-April,the total rainfall,total rain days,average temperature in mid-May as the keymeteorological factors in the north of Hebei Plain;The total rainfall and total rainy days in late April,the total rainy days in mid-May as the key meteorological factors in the middle of Hebei Plain;The total rainy days in mid-March,the average temperature in mid-March and late April,the average humidity in early May as the main factor in the South of Hebei Plain,The prediction models for the southern,central and northern regions respectively were built.According to the review of historical data,the prediction accuracy of the three models was 78.6%,77.8% and 80.0% respectively,which were of good prediction.3.By screening typical sites,the analysis of climatic characteristics of wheat leaf rust in different years inHebei Plain shows that:(1)Air temperature year type: 1.Severe epidemic year type: temperature rose quickly in the early of the year,and was higher than the average annual temperature from mid-March to late April.2.Moderately epidemic year type: temperature rose slowly in March and was lower than normal.(2)Rainfall type: 1.Severe epidemic year type: there was more rainfall from late March to May.2.Moderately epidemic year type: the rainfall in April or May was significantly lower than usual.4.Developed a wheat leaf rust intelligent assessment system,which includes technical information,expert message,disease monitoring,disease identification,and other modules,in which the disease monitoring module can predict the degree of disease occurrence in the sub-region of the whole province,the disease detection part can rapidly identify the rust,and determine the size of the scab,to achieve batch self-service calculation of leaf disease rate,disease level and disease index.It has been verified that the efficiency of this system is improved by 85% compared with manual recognition,and the misjudgment rate is also greatly reduced.