花龄期棉花虫害的电子鼻检测  

Electronic nose for detection of cotton pests at the flowering stage

作　　者：周博[1] 代雨婷 李超 王俊[2] Zhou Bo;Dai Yuting;Li Chao;Wang Jun(Department of Mechanical Engineering,Yancheng Institute of Technology,Yancheng 224051,China;College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310029,China)

机构地区：[1]盐城工学院机械工程学院,盐城224051 [2]浙江大学生物系统工程与食品科学学院,杭州310029

出　　处：《农业工程学报》2020年第21期194-200,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(31671583)。

摘　　要：棉花害虫具有隐蔽性、迁飞性和突发性特点,并且影响因素众多,棉花虫害准确地诊断是农业领域的难点问题。该研究以受到棉铃虫侵害的花铃期棉花为研究对象,采用电子鼻对不同处理的棉花挥发物进行检测。研究表明,主成分分析(Principal Component Analysis,PCA)和聚类分析结果显示健康棉花释放的挥发物具有明显的昼夜节律性,健康棉花与虫害棉花差异性显著。径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)对8个不同时间的4组虫害棉花处理进行分析,测试集判别总的正确率为73.4%,健康棉花对照组测试集判别正确率100%,误判样本出现在3个虫害处理之间。当不考虑时间因素建立虫害棉花统一的预测模型,RBFNN模型对健康棉花对照组的预测正确率均达到了100%,分析结果可以作为花铃期棉花是否遭受棉铃虫侵害的依据,说明电子鼻可以作为棉花虫害发生的有效监测手段,在农作物虫害监测领域具有潜在的应用价值。The cotton pests have the characteristics of concealment,migration,and sudden burst,and there are many influencing factors involved.The accurate diagnosis of cotton pests is a difficult problem in the agricultural field.Previous studies have demonstrated that cotton plants produce blends of volatile compounds in response to herbivores serve as cues for parasitic and predatory insects.Therefore,it is possible to obtain information about cotton pests by detecting volatile compounds in cotton.In this study,an electronic nose was used to detect the volatiles emitted by cotton plants damaged by cotton bollworm at the flowering period.The cotton samples were divided into four infested cotton treatments.According to the number of pests in each pot of cotton seedlings,the treatments inoculated with 0,1,2,and 3 bollworm larvae were marked as 0-P,1-P,2-P,and 3-P,respectively.The 0-P was healthy cotton as a control treatment.The cotton bollworm feeding lasted 48 h.During this period,the electronic nose detection tests were performed every 6 h,and a total of 8 repeated tests were performed.Appropriate pattern recognition techniques were applied to construct reliable algorithms for interpreting the acquired signal in cotton.Principal Component Analysis(PCA),discriminant function analysis,cluster analysis,and Radial Basis Function Neural Network(RBFNN)were applied to evaluate the data.The results of PCA and discrimination values of the healthy cotton treatment showed that the volatiles released by healthy cotton had obvious circadian rhythm.For the three infested cotton treatments,whereas the distribution patterns of cotton samples were different from that of the healthy cotton treatment.The three infested cotton treatments had regular distribution trends that cotton samples changed along the direction of the first and second principal components.Cluster analysis results showed that the four cotton treatments were all finally divided into two categories,the healthy cotton treatment,and the three infested cotton treatments.All

关 键 词：电子鼻 神经网络 预测 棉花 棉铃虫 模式识别 

分 类 号：S224.3[农业科学—农业机械化工程]