红外与毫米波学报2009，Vol.28Issue(4)：272-276,5.
基于可见/近红外光谱技术的黄瓜叶片SPAD值检测
DETECTION OF SPAD VALUE OF CUCUMBER LEAVES BASED ON VISIBLEF/NEAR INRARED SPECTROSCOPY TECHNIQUE
摘要
Abstract
For the rapid detection of SPAD value of cucumber leaves, the calibration model of SPAD value was built by using visible and near infrared (Vis/NIR) spectroscopy technique and chemometrics methods.Different calibration methods were used to build the model in the whole wavelength region.The results indicate that the optimal performance is achieved by least squares support vector machine(LSSVM) model, and the correlation coefficient (r) and root mean squares error of prediction (RMSEP) are 0.9583 and 0.9732, respectively.Via the analysis of correlation coefficients between the spectral reflectance and SPAD values, and the regression coefficients of partial least squares ( PLS) , two characteristic wavelength bands (531 ～581nm and 696 ～716nm) and four characteristic wavelengths (556, 581, 698 and 715nm) were obtained.LSSVM was used to the aforementioned wavelength bands and wavelengths.The results indicate that the characteristic wavelength bands can achieve a better performance with r of 0.9338 and RMSEP of 1.1370.The prediction results are similar to the whole wavelength region model, while, the performance of LSSVM model with four characteristic wavelengths was not satisfying.Calibration method of using characteristic wavelength bands can largely reduce the calibration computation, and increase the calibration speed.Hence, it is more effective to use characteristic wavelength bands for the detection of SPAD values of cucumber leaves.关键词
黄瓜/可见/近红外光谱/最小二乘支持向量机/叶绿素Key words
cucumber/Vis/NIR spectroscopy/least squares support vector machine (LSSVM)/chlorophyll分类
农业科技引用本文复制引用
刘飞,王莉,何勇,鲍一丹..基于可见/近红外光谱技术的黄瓜叶片SPAD值检测[J].红外与毫米波学报,2009,28(4):272-276,5.基金项目
国家高技术研究发展计划(863计划)(2007AAIOZ210)和国家自然科学基金(30671213)资助项目 （863计划）
