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An Artificial Neural Network#8211;based Expert System for Fruit Tree Disease and Insect Pest Diagnosis

机译:基于人工神经网络的果树疾病和害虫诊断专家系统

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This paper reports the development of an expert system for fruit tree disease and insect pest diagnosis based on artificial neural network (ANN) and geographic information system (GIS). A multiple knowledge acquisition approach was adopted, consisting of interview expert, questionnaire, web-based search and literature review. The production rule was adopted as the formation of knowledge representation in the system. The reasoning process adopted a control method of depth precedence. In the prediction subsystem, the MATLAB neural network toolbox was used to predict the development tendency of fruit tree disease and insect pest. The subsystem was trained with 11 years' meteorological information and occurrence status of fruit tree disease and insect pests in orchards of Yantai city. The ring spot, a fruit tree disease, was chosen as the research object to compare the predicted value with the actual value in this study. A GIS platform (ArcInfo) can provide the functions of spatial and temporal analysis and was used to analyze and display the development tendency of fruit tree disease and insect pests. Preliminary results in developing a web-based expert system for fruit tree disease and insect pest diagnosis are also summarized.
机译:本文报告了基于人工神经网络(ANN)和地理信息系统(GIS)的果树疾病和害虫诊断专家系统的发展。采用了多种知识获取方法,包括面试专家,问卷,基于网络的搜索和文献综述。制作规则是作为制度中知识代表的形成。推理过程采用了一种深度优先级的控制方法。在预测子系统中,MATLAB神经网络工具箱用于预测果树疾病和害虫的发展趋势。该子系统接受了烟台市果树病和果树病和昆虫害虫的11年的气象信息和发生状态。选择果树疾病的环斑作为研究对象,将预测值与本研究中的实际价值进行比较。 GIS平台(ArcInfo)可以提供空间和时间分析的功能,用于分析和显示果树疾病和害虫的发展趋势。还概述了开发基于Web的果树疾病和害虫诊断的基于Web的专家系统的初步结果。

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