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.