Research and application of rice FT-AI-GSCMPP pest control model

In order to effectively improve the efficiency of rice pest prevention and control, reasonably control the rice population density and pest population density, a generalized system control model for rice pest population based on artificial intervention factors（AI-GSCMPP） was established by improving the generalized system model for rice pest population（GSMPP） by adding factors such as removing diseased leaves and spraying pesticides to the model. Meanwhile, in order to solve the problem of multiple variables and difficult implementation of AI-GSCMPP, this study introduced fuzzy theory based on AI-GSCMPP to implement nonlinear intelligent control of rice pests, and established a fuzzy theory based artificial intervention factor generalized system control model for rice pest population（FT-AI-GSCMPP）. A series of experiments and production applications have been carried out in Shanghai Songjiang Rice Planting Base. The results show that the control effect of FT-AI-GSCMPP on rice weevil is 95.67%, and the control effect of leaf roll is 94.12%, reaching the best of five models; the decay rate of FT-AI-GSCMPP to the armyworm, aphid and rice gall midge reach 91.84%, 90.68% and 92.65% respectively, leading the other four models. The application of FT-AI-GSCMPP in rice production shows that the seed setting rate, number of grains per panicle, number of panicles, 1 000-grain weight, and actual yield of rice reach 93.38%, 91.04 grains, 345 800 panicles/667 m~2, 28.16 g, and 686.37 kg/667 m~2, respectively, which are the best among the five models. FT-AI-GSCMPP is superior to other pest control models in terms of pest control efficiency, population reduction rate, leaf roll control efficiency, seed setting rate and other indicators. The model has the characteristics of high control efficiency, small error, strong stability, and can be used in research fields such as rice pest control.