There is existence of poor universality and low prediction precision in citrus canker hyperspectral models in previous research. It is necessary to investigate an approach to improve the robustness of hyperspetral model transfer between different instruments which proposed to improve the robustness of the calibration model. Hyperspectral images of two different varieties including Navel Orange 52andCaraCarawere acquired using a laboratory hyperspectral imaging system (System 1, S1) and a portable hyperspectral imaging system (System 2, S2). The discriminant models for the citrus canker detection were developed based on the images from S1 and S2, respectively, and different pretreatment and classification methods were also investigated. Meanwhile, direct standardization (DS) algorithm was used to calibrate hyperspectral images collected by S2 which was considered as the slave while S1 as the master, and the performance of the discriminant model were evaluated before and after the model transfer. It was shown that the best discriminant results were achieved by the extreme learning machine (ELM) combined with the second-order derivative with the classification accuracies of 97.5% by S1 and 98.3% by S2, respectively. By using DS, the classification accuracy increased from 38.1% to 86.2% after the model transfer. It is demonstrated that the DS algorithm is useful for the calibration model transfer between different instruments, which would be helpful for developing a robust method for the citrus canker detection.