高光谱图像技术作为农产品无损检测的新技术，探讨了其在柑橘外部品质检测的可行性。以检测柑橘果锈为目的，首先对经预处理的高光谱图像数据进行主成分分析，优选出571 nm、652 nm 和741 nm 三个特征波长组成新的图像块；再进行第二步主成分分析，得到的第三主成分图像为最适宜检测柑橘果锈的图像；最后对该图像进行中值滤波、平方根变换、阈值分割和数字形态学运算完成特征提取。试验结果表明，此算法对柑橘果锈检测的正确率可达到90%。研究表明，利用高光谱图像技术结合两步主成分分析算法检测柑橘果锈是可行的。

Hyperspectral imaging technology, a new Non-destructive Testing (NDT) technology of agricultural products, was attempted to detect external quality of citrus. Detection of rust in citrus was studied in this work. Firstly, Principal Component Analysis (PCA) was performed on hyperspectral imaging data after preprocessing to determine three dominant wavelengths (i.e. 571, 652, 741 nm). Secondly, the datacube composed of three dominant wavelength images was analyzed based on PCA again, and the third Principal Component (PC3) image was found to be most suitable for identifying the presence of rust. Finally, rust features on citrus’s surface were segmented by median filtering, square root transformation, determination of optimal threshold and morphological imaging processing. Experimental results show that the rust in citrus can be detected with an accuracy of 90%. This work demonstrates that it is feasible to detect rust in citrus by using the hyperspectral imaging technology and twice steps PCA.