中文摘要：

庄稼健康条件的察觉在做庄稼疾病和昆虫损坏的控制策略并且在迟了的生长阶段获得高质量的生产起一个重要作用。在这研究，米饭圆锥花序的 hyperspectral 反射在可见、在红外线附近的区域被测量。圆锥花序根据健康条件被划分成三个组：健康圆锥花序， Nilaparvata lugens St&Oal 引起的空圆锥花序，和感染 Ustilaginoidea virens 的圆锥花序。低顺序衍生物系列，也就是第一和第二份订单，用不同技术被获得。主要部件分析(PCA ) 被执行获得前述的衍生物的主要部件系列(PCS ) 和未加工的系列减少反射光谱尺寸。支持向量分类(SVC ) 被采用区别健康，空，并且感染的圆锥花序，与前面三 PCS 作为独立变量。全面精确性和 kappa 系数被用来估计 SVC 的分类精确性。有 PCS 的 SVC 的全面精确性源于未加工，首先并且第二为严峻的数据集的反射系列是 96.55% ， 99.14% ，和 96.55% ，并且 kappa 系数分别地是 94.81% ， 98.71% ，和 94.82% 。我们的结果证明使用可见、在红外线附近的光谱学区别米饭圆锥花序的健康条件是可行的。关键词瑞斯圆锥花序 - 主要部件分析(PCA )- 支持向量分类(SVC )- Hyperspectral 反射 - 衍生物系列 CLC 数字 TP7 - 中国(号码 2010CB126200 ) 的国家基本研究节目(973 ) 支持的 S43 工程和中国博士后的科学基础工程(号码 20090451437 )

英文摘要：

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St~l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.