CAJ | 학술논문

采用近红外高光谱成像技术对菜青虫的存活与死亡状态进行了研究，通过提取菜青虫不同状态的光谱信息，建立判别分析模型。以不同预处理方法对所提取的951．5～1649．2 nm光谱进行预处理，并建立偏最小二乘判别分析（partial least square-discriminant analysis，PLS-DA）模型对菜青虫的生死状态进行判别分析，判别正确率接近或达到100％。用移动平均（moving average，MA）5点平滑光谱分别采用连续投影算法（successive projections algorithm，SPA）以及加权回归系数（weighted regression coefficient，Bw ）分别选取了17和20个特征波长进行生与死状态的判别。基于特征波长建立了 PLS-DA，K 最邻近节点算法（K-nea-rest neighbor，KNN），BP神经网络（back propagation neural network，BPNN）以及支持向量机（support vec-tor machine，SVM）模型，判别正确率接近100％。结果表明采用近红外高光谱成像技术对菜青虫生命状态的研究是可行的，为作物虫害的快速诊断提供了新方法。
채용근홍외고광보성상기술대채청충적존활여사망상태진행료연구，통과제취채청충불동상태적광보신식，건립판별분석모형。이불동예처리방법대소제취적951．5～1649．2 nm광보진행예처리，병건립편최소이승판별분석（partial least square-discriminant analysis，PLS-DA）모형대채청충적생사상태진행판별분석，판별정학솔접근혹체도100％。용이동평균（moving average，MA）5점평활광보분별채용련속투영산법（successive projections algorithm，SPA）이급가권회귀계수（weighted regression coefficient，Bw ）분별선취료17화20개특정파장진행생여사상태적판별。기우특정파장건립료 PLS-DA，K 최린근절점산법（K-nea-rest neighbor，KNN），BP신경망락（back propagation neural network，BPNN）이급지지향량궤（support vec-tor machine，SVM）모형，판별정학솔접근100％。결과표명채용근홍외고광보성상기술대채청충생명상태적연구시가행적，위작물충해적쾌속진단제공료신방법。
Near-infrared hypserspectral imaging technology was applied for the discrimination of a variety of life states,the judg-ment of being alive or death.Discrimination models were built based on spectral data of pieris rapaes acquired during different life states.The wavelengths from 951. 5 to 1 649. 2 nm were used for analysis after the removal of spectral region with obvious noi-ses at the beginning and the end.And the spectra data of 951. 5~1 649. 2 nm were preprocessed by different pretreatment meth-ods.To discriminate the state of being alive or death of pieris rapaes,discrimination models were built based on the spectral data processed by different pretreatment methods.Results showed that the discriminant accuracy can approach or attain 100%.Thus the method was proved to be useful for the discrimination of the state of being alive or death of pieris rapaes.After the spectral data were preprocessed by moving average (MA)algorithm,17 characteristic wavelengths were extracted based on weighted re-gression coefficient (Bw)and 20 were extracted based on successive projections algorithm (SPA)to identify the state of being a-live or death of pieris rapaes.Four classification methods based on characteristic wavelengths,including partial least squares-dis-criminant analysis (PLS-DA),K-nearest neighbor algorithm (KNN),back propagation neural network (BPNN)and support vector machine (SVM)were used to build discriminant models for identifying the state of being alive or death of pieris rapaes. The discriminant accuracy all can approach or attain 100%.