基于BP神经网络的稻瘟病预测预报研究  

Investigation of Rice Blast Prediction and Forecast Based on BP Neural Network

作　　者：刘庭洋[1] 李烨[1] 浦仕磊[1] 李志宇[1] 李文静[1] 吴奇[1] 王云月[1] LIU Ting-yang LI Ye PU Shi-lei LI Zhi-yu LI Wen-jing WU Qi WANG Yun-yue(Key Laboratory of Biological Diversity to Control Pests and Diseases, Ministry of Education/College of Plant Protection, Yunnan Agricultur- al University, Yunnan Kunming 650201, China)

机构地区：[1]云南农业大学植物保护学院/教育部生物多样性与病害防控重点实验室,云南昆明650201

出　　处：《西南农业学报》2017年第7期1546-1553,共8页Southwest China Journal of Agricultural Sciences

基　　金：云南省现代农业水稻产业技术体系

摘　　要：【目的】稻瘟病是水稻主要病害之一,严重制约水稻高产稳产。近年来随着品种布局、耕作制度改变及气候变化的影响,其流行程度年度间波动很大。目前,稻瘟病在云南省各水稻产区呈现中等偏重发生的趋势,预测预报作为指导防治的先行者,具有重要作用。【方法】为了及时有效的做好稻瘟病防范工作,本研究采用稻瘟病发生相关气象因子及田间穗瘟病情指数,利用BP神经网络技术,选取德宏州芒市为试验点开展稻瘟病预测预报研究。【结果】从气象因子与预测对象的相关性来看,筛选出来的各气象因子与病情指数之间都存在较强的相关性,其理想输出和实际输出值都比较接近,误差曲线也比较吻合,预测准确度能满足实际需求。【结论】由此可见,BP神经网络建立的稻瘟病中期预测模型更具有优势。不需要事先进行数学公式表达,具有更高的预测准确度,选择试验点5-9月的气象数据以及田间稻瘟病病情指数建立的预测预报模型,预测结果更为客观和可靠,能及时做好稻瘟病的防控工作。[ Objective ] Rice blast was one of the major diseases of rice, and seriously restricted stable high yield of rice. With the influence of varieties layout, cropping system and climate change in recent years, the prevalence of inter-annual fluctuation was very large. Currently, the rice blast showed a trend towards the middle to high of the rice-producing areas in Yunnan Province. Prediction and forecast played an important role as a pioneer of the guide of prevention and control. [ Method] In order to timely and effective do a good job of rice blast pre- vention work, we adopted its related meteorological factors and field rice panicle blast disease index, used BP neural network technology, se- lected Dehong prefecture Mang city as a test point to cm＇ry out the prediction and forecast research. [ Result] From the relevance of meteoro- logical factor and forecasting object, there was a strong correlation between various meteorological factors by screening and the disease index, the ideal and actual output values were closer, error curve was consistent, prediction accuracy could satisfy the actual demand. [ Conclusion] The medium-term prediction model of the rice blast disease established by BP neural network was more advantageous, that was no need for a mathematical formula to express in advance, had a higher prediction accuracy. The prediction model was established by 5 - 9 meteorological data of experimental sites and disease index of rice blast in the field were more objective and reliable, and could do a good job of disease prevention and control.

关 键 词：稻瘟病 BP神经网络 预测预报 

分 类 号：S435.111.41[农业科学—农业昆虫与害虫防治]