基于故障树分析法的柑橘病虫害诊断专家系统
作者:
作者单位:

作者简介:

通讯作者:

基金项目:

国家自然科学基金(31671591);广东省现代农业产业技术体系创新团队建设专项资金(2021KJ108);广州市科技计划项目(202002030245);财政部和农业农村部:国家现代农业产业技术体系资助项目(CARS-26)


An expert system for diagnosing citrus diseases and pests based on fault tree analysis
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    目的 为解决柑橘病虫害防治工作中植物保护主业知识普及不足、高效病虫害诊断手段缺乏的问题,研制一种基于故障树分析法的柑橘病虫害诊断专家系统。方法 首先,利用故障树分析法计算病虫害发生的概率、建立病虫害知识库;其次,根据知识库以及正向推理策略设计实现专家系统的推理机;最后,利用微信开发者工具在微信小程序内搭建故障树分析法的计算规则、专家系统的推理机,构建基于故障树分析法的专家系统。结果 建立了一种包含病虫害模块、最新资讯模块、知识库查询模块、病虫害诊断模块、用户中心模块5大功能模块的柑橘病虫害诊断专家系统。经测试,系统可以在不同型号的手机中平稳运行,占用内存的平均大小为175 MB,系统启动的平均耗时为1.0984 s,页面切换的平均耗时为0.0495 s;连续运行系统1 h,手机与服务器的连接均未出现异常。结论 本系统运行稳定可靠、页面样式显示正常。用户通过此系统能够诊断病虫害并获得具体的防治技术,同时也能够了解专业的植物保护知识。

    Abstract:

    Objective An expert system for diagnosing citrus diseases and pests based on fault tree analysis was developed to solve the problems of insufficient popularization of professional plant protection knowledge and lack of efficient diagnosis methods in the prevention and control of citrus diseases and pests. Method First, fault tree analysis method was used to calculate the occurrence probability of diseases and pests and establish a knowledge base of diseases and pests. Secondly, based on the knowledge base and forward reasoning strategy, the reasoning engine of the expert system was designed and implemented. Finally, Weixin DevTools were used to equip the calculation rules of fault tree analysis method and the inference engine of expert system in weixin mini program, and build the expert system based on fault tree analysis method. Result We established an expert system for diagnosing citrus diseases and pests with five functional modules: Pest knowledge module, latest information module, knowledge base query module, diseases and pest diagnosis module as well as user center module. After testing, the system could run smoothly in different types of mobile phones. The average size of memory occupied was 175 megabytes, the average time for system startup was 1.0984 s and the average time for page switching was 0.0495 s. After running the system continuously for 1 h, the connection between the mobile phone and the server was normal. Conclusion The system is stable and reliable, and the page style is displayed normally. Users can exploit the system to diagnose diseases and pests and obtain corresponding control methods. Meanwhile, users can also learn professional plant protection knowledge from the system.

    参考文献
    相似文献
    引证文献
引用本文

杜盼,孙道宗,李震,宋淑然.基于故障树分析法的柑橘病虫害诊断专家系统[J].华南农业大学学报,2022,43(4):106-112

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
历史
  • 收稿日期:2021-10-31
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2022-07-06