Abstract: 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, 345800 panicles/667m2, 28.16g, and 686.37kg/667m2, 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.

Key words: rice, pest prevention and control, insect control effect, antiroll effect, fuzzy theory； plant protection

摘要： 为有效提升水稻虫害防控工作效率，合理控制水稻种群密度、虫害种群密度，在水稻虫害种群广义系统模型(GSMPP)的基础上进行改进，将清除病残叶和喷洒农药等因子加入模型，建立基于人工干预因子水稻虫害种群广义系统控制模型(AIGSCMPP)。为解决AIGSCMPP涉及变量多、实施难度大问题，在AIGSCMPP的基础上引入模糊理论，对水稻虫害实施非线性智能防控，建立基于模糊理论的人工干预因子水稻虫害种群广义系统控制模型(FTAIGSCMPP)。在上海松江水稻种植基地开展系列试验和生产应用，试验结果显示FTAIGSCMPP对稻象甲的虫量防效为95.67%，卷叶防效为94.12%，达到五种模型最优；FTAIGSCMPP对黏虫、蚜虫、稻瘿蚊的虫口减退率分别达到91.84%、90.68%和92.65%，领先于其他四种模型。水稻生产应用结果显示，FTAIGSCMPP对水稻的结实率、每穗粒数、穗数、千粒重、实际产量分别达到93.38%、91.04粒、34.58万穗/667m2、28.16g、686.37kg/667m2，在五种模型中表现最佳。FTAIGSCMPP的虫量防效、虫口减退率、卷叶防效、结实率等指标领先于其它虫害防控模型，该模型具有防控效率高、误差小、稳定性强等特点，可用于水稻虫害防控等研究领域。

关键词: 水稻, 虫害防控, 虫量防效, 卷叶防效, 模糊理论, 植物保护