Research On The Prediction Of Peach Disease And Pest Injury Based On Fuzzy Control And RBF Neural Networks

The peach originating in China has more than 4,000 years of history in our country. The peach is delicious and has rich nutrition. It is very popular in the consumption market. The peach is one of the most important nuts kinds of fruit trees. The peach is adaptable, spread widely, manage easy, and has high yields. Some better managed gardens stably maintain the 2,500 kilos per mu. By 2009, the planted area of the peach had reached 722,800 hectares and the total output of the peach is 8,052,000 tons in China. Cultivated area and output of the peach of China both rank the first in the world. Peach is one of the major agricultural byproducts of the Hebei Province Shunping area. However, the peach is easy to suffer from some plant diseases and pest injury in he production process, such as sphaceloma,the disease of flowing glue,xanthomona campestns (Smith) dovosen,myzus persicae and so on. The diseases and pest injury seriously impact the peach to grow and the quality and productivity. It is a threat for the development of peach industry constitute. To forecast the future of peach disease and pest injury can make the prevention active to have a purpose, plan and key. Only when the prediction is betimes and accurately, people can draw out the colligated plan and take the effective measure to reduce the quantity of peach diseases and pest injury, then ensure peach product fruitful.The prediction of the peach diseases and pest injury is a typical nonlinear system, and there are also many factors to result in plant disease and pest injury. Therefore, using the conventional means to predict is very difficult. In recent years the artificial neural network methods etc have height nonlinear to reflect the ability of shoot. Its characteristic is that it dose not leave to know the mathematical model of the objects, but rather solves the problems by the example training. Especially RBF (Radial Basis Function) network has good skills, used to promote in a complex functions of the relationship between the question when a letter with a high accuracy. But many intelligent way lack of the general rule that instruct model to chooses automatically in the speed, stability and the overall situation to order smallest aspects of studying to refraining from rash action. So this text put fuzzy control to the RBF neural network, resolve the above-mentioned problem better.The main works this paper done as follow:(1) This text compares RBF neural network and basic BP neural network, and points out the shortcomings of the basic BP neural network, embodies the difference between two networks.(2) According to the actual circumstances of the peach plantation, after calculating the factors such as temperature, weather status etc which influencing the forecast of disease and pest injury. The article conducts the RBF network model establishment, the training, the simulation based on the MATLAB neural network toolbox.(3)To predict the Shunping area the occurring degree of the disease and pest injury inspects the effect of the model. The results indicate that the forecasting method of fuzzy control and RBF nerve network integration is more accurate, accordingly it shows the effectiveness and practicability of the method, and has a better applied foreground.