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Aiming at the problem that the symptoms of early wheelspot disease and rust disease on sugarcane leaves are similar and difficult to distinguish, which leads to the inconvenience of prescribing the right medicine to the disease in actual production. The feasibility of using hyperspectral imaging technology to identify early wheel spot disease and rust disease on sugarcane leaves was explored. Firstly, hyperspectral images of healthy sugarcane leaves, early wheel spot leaves and rust leaves were collected by hyperspectral imaging system in the spectral range of 406~1014nm. The average spectral reflectance of region of interest (ROI) was extracted and its average spectrum was calculated as the raw spectral data. The first derivative (FD), Savitzky-Golay convolution smoothing (SG) and standard normal variate (SNV) were used to preprocess the original spectral data. Then on the basis of preprocessing, principal component analysis (PCA) and ant colony optimization (ACO) were used to reduce the feature dimension, and the feature after dimensionality reduction were used as the input variables in the later modeling. Finally, the support vector machine (SVM) and random forest (RF) were used for recognition by combining dimensionality reduction and non-dimensionality reduction. In order to determine the optimal recognition model, totally 18 combined models with different preprocessing methods, dimensionality reduction methods and classifiers were tested. By comparison, it was found that the SG-SVM recognition model had the best effect, and the accuracy of the test set was 99.65%. It was feasible and effective to use hyperspectral imaging technology to identify early wheel spot and rust on sugarcane leaves, which can provide reference for ultra-low altitude remote sensing disease monitoring of plant protection UAV.