Abstract: In citrus orchards ,manganese (Mn) and/or zinc (Zn) deficiencies are very common ,while the symptoms caused by them are difficult to identify ,w hich usually results in a w rong treatment during or‐chard management .In order to lay a foundation for application of hyperspectral imaging technology to the identification of nutrition deficiency in citrus ,an experiment was made ,in which the spectral response characteristics of ‘Hamlin’ Sweet Orange leaves lacking Mn and/or Zn in different extents were character‐ized with hyperspectral imaging technology ,and then two recognition models were built using Fisher linear discriminant analysis (LDA) and least squares support vector machine (LSSVM ) ,the recognition accuracy of which was subsequently compared .Our results showed that the model built by LSSVM with the whole spectral range wavelength gave a recognition accuracy of 91.88% in the modeling set and 90.00% in the prediction set ;in addition ,the model built by LSSVM with forty wavelengths selected by successive pro‐jections algorithm (SPA) resulted in a recognition accuracy of 82.50% .The results in this study indicated that it w as feasible to identify M n and/or Zn deficiency using hyperspectral imaging technology .