提出了基于连续投影算法（successive projections algorithm, SPA）、 载荷系数法（x-loading weights, x-LW）和格拉姆-施密特正交（gram-schmidt orthogonalization, GSO）提取特征波长的高光谱成像技术检测番茄叶片早疫病的方法。 首先获取380～1 023 nm波段范围内70个健康和70个染病番茄叶片的高光谱图像信息, 然后提取健康和染病叶片感兴趣区域(region of interest, ROI)的光谱反射率值, 建立番茄叶片早疫病的最小二乘-支持向量机(least squares-support vector machine, LS-SVM)鉴别模型, 建模集和预测集的鉴别率都是100%。 再通过SPA 、 x-LW和GSO提取特征波长（effective wavelengths, EW）, 并建立EW-LS-SVM和特征波长-线性判别分析（ew-linear discriminant analysis, EW-LDA）鉴别模型。 结果显示, 每个模型的鉴别效果都很好, EW-LS-SVM模型中预测集的鉴别率都达到了100%, EW-LDA模型中预测集的鉴别率分别是100%, 100%和97.83%。 基于SPA, x-LW和GSO所建模型的输入变量分别是4个（492, 550, 633和680nm）, 3个（631, 719和747 nm）和2个（533和657 nm）, 较少的特征波长便于实时检测仪器的开发。 结果表明, 高光谱成像技术检测番茄叶片早疫病是可行的, SPA, x-LW和GSO都是非常有效的特征波长提取方法。

Identification of early blight on tomato leaves by using hyperspectral imaging technique based on different effective wavelengths selection methods (successive projections algorithm, SPA; x-loading weights, x-LW; gram-schmidt orthogonalization, GSO) was studied in the present paper. Hyperspectral images of seventy healthy and seventy infected tomato leaves were obtained by hyperspectral imaging system across the wavelength range of 380～1023 nm. Reflectance of all pixels in region of interest (ROI) was extracted by ENVI 4.7 software. Least squares-support vector machine (LS-SVM) model was established based on the full spectral wavelengths. It obtained an excellent result with the highest identification accuracy (100%) in both calibration and prediction sets. Then, EW-LS-SVM and EW-LDA models were established based on the selected wavelengths suggested by SPA, x-LW and GSO, respectively. The results showed that all of the EW-LS-SVM and EW-LDA models performed well with the identification accuracy of 100% in EW-LS-SVM model and 100%, 100% and 97.83% in EW-LDA model, respectively. Moreover, the number of input wavelengths of SPA-LS-SVM, x-LW-LS-SVM and GSO-LS-SVM models were four (492, 550, 633 and 680 nm), three (631, 719 and 747 nm) and two (533 and 657 nm), respectively. Fewer input variables were beneficial for the development of identification instrument. It demonstrated that it is feasible to identify early blight on tomato leaves by using hyperspectral imaging, and SPA, x-LW and GSO were effective wavelengths selection methods.