基于茎干含水率的紫薇病虫害等级早期诊断方法  

Early Diagnosis Method of Disease and Pest Level on Lagerstroemia indica Based on Stem Water Content

作　　者：高超[1,2] 赵玥 赵燕东[1,2] GAO Chao;ZHAO Yue;ZHAO Yandong(School of Technology,Beijing Forestry University,Beijing 100083,China;Beijing Laboratory of Urban and Rural Ecological Environment,Beijing Municipal Education Commission,Beijing 100083,China;Key Laboratory of State Forestry Administration for Forestry Equipment and Automation,Beijing 100083,China)

机构地区：[1]北京林业大学工学院,北京100083 [2]城乡生态环境北京实验室,北京100083 [3]林业装备与自动化国家林业局重点实验室,北京100083

出　　处：《农业机械学报》2018年第11期189-194,250,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点研发计划项目(2017YFD0600901);北京市科技计划项目(Z161100000916012);北京市共建项目

摘　　要：为了对植物病虫害进行早期预警,提出一种基于茎干含水率的植物病虫害等级早期诊断方法。以紫薇为研究对象,监测复苏萌芽期内不同健康等级紫薇的茎干含水率;然后,分别通过关键参数和主成分分析对茎干含水率进行特征提取;最后,结合有监督和无监督学习模型实现对紫薇病虫害等级的早期诊断。基于方差分析,紫薇健康等级对日最小含水率、日最大含水率、日平均含水率、日极差含水率4个关键参数的影响均为极显著。基于主成分分析,茎干含水率时间序列前4个主成分的累计贡献率达到99. 7%。在有监督模型中,以主成分特征为输入的BP模型的性能最优,平均识别率达到98%;在无监督模型中,以主成分特征为输入的K均值模型最优,平均识别率达到92%。因此,茎干含水率可以作为诊断植物病虫害等级的早期指标,主成分特征优于关键参数特征,有监督模型优于无监督模型。A new method was proposed for early diagnosis of disease and pest level based on stem water content,which provided early warning for diseases and pests.Lagerstroemia indica seedlings with different health levels were monitored for acquiring stem water content.Then the features of stem water content were respectively extracted by two methods,including key parameter and principle component analysis.Ultimately,some supervised and unsupervised models were established for early diagnosis of disease and pest level on Lagerstroemia indica.Judging from variance analysis,the effects of health level on four key parameters(daily minimum,maximum,average and range of stem water contents)were all in very significant difference.Judging from principle component analysis,the cumulative contribution rate of the first four principal components of stem water content reached 99.7%.Among supervised models,BP model with input of PCA features performed the best and its average recognition reached 98%.Among unsupervised models,K-means model with input of PCA features performed the best and its average recognition rate reached 92%.Hence,stem water content can be chosen as a reliable index for early diagnosis of plant disease and pest level.The PCA features were superior to the key parameter features.The performance of supervised models was better than that of unsupervised models.

关 键 词：紫薇 茎干含水率 病虫害等级 早期诊断 特征提取 学习模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]