As one of the major factors hindering crops growth, crop diseases make more than 12% loss of crop yield annually. Diseases not only directly reduce crop yields, but also seriously debase the quality of agricultural products, and even cause food safety accidents. Spectral imaging technique is aninformation foraging approach that fuses image processing and spectroscopy. It couldobtain image and spectral information of crop diseases simultaneously and describediseased spots feature intuitively. Spectral imaging technology improves the accuracy and efficiency of crop disease detection because of the advantage of union of imagery and spectrum and has been a hotspot at present research. This paper reviews the related literatures in recent six years, and analyses the advantages and limitations of spectral imaging technology in crop disease detection and focuses on the third key technology of spectral imaging in crop disease detection. The third key technology of spectral imaging in crop disease detection is emphasized: (1) Spectral image segmentation technology, focusing on the advantages and application scope analysis of four common segmentation algorithms; (2) spectral feature and spatial feature extraction technology, focusing on the accuracy comparison of spatial features, spectral features and their weighted combination of disease information expression; (3) detection model, focusing on the stability and prospects of spectral vegetation index and machine learning model in crop disease detection. Finally, this paper prospects the application prospect and research trend of spectral imaging technology in the field of crop disease detection, and provides a comprehensive and systematic reference for related research.