Tomato early blight is highly infectious and destructive. The detection and identification of pre-symptom characteristics in the incubation period is the key to Tomato Early Blight monitoring, early warning and scientific control. In this paper, the evolution of early tomato blight was monitored by hyperspectral images, and the data were analyzed combined with visible light images and spectral characteristics. The results showed that the average value of near-infrared spectrum and red edge reflectance of tomatoes infected with early blight decreased with time, and the disease information of the incubation period appeared 36 hours after inoculation. This paper selected the spectral data of 36 h inoculation as the modeling data of Tomato Early Blight incubation period. The principal component (PCA) transformation and multivariate scattering correction (MSC) were used to reduce the spectral dimension or noise of the modeling data. Then the gradient lifting decision tree (GBDT) and support vector machine (SVM) recognition models were established, and the data were imported for training and recognition. The influence of PCA and MSC preprocessing methods on the recognition effect of gradient lifting decision tree (GBDT) and support vector machine (SVM) models is discussed. The influence of common kernel functions on SVM recognition models is further discussed, and the combination algorithm of preprocessing method and recognition model is optimized. The results showed that the accuracy of PCA-GBDT, PCA-SVM (Gaussian kernel), PCA-SVM (linear kernel), MSC-GBDT and MSC-SVM (polynomial kernel) was more than 95%, which could well realize the spectral recognition of Tomato Early Blight incubation period; Among them, MSC-GBDT has the best recognition recall and accuracy, while PCA-SVM (Gaussian kernel) has the highest recognition efficiency. The research shows that the hyperspectral data of the Tomato Early Blight incubation period after noise reduction reduces the noise is more in line with the real distribution, and has a large amount of data. The recognition ability will be insufficient, while combined with a complex recognition model, a higher test result can be achieved; The dimension reduction algorithm can reduce the dimension and amount of hyperspectral data in the incubation period of early tomato blight, and the features after dimension reduction can express the lesion information. When combined with a simple recognition model, the recognition effect is good, while with an overly complex recognition model, it will lead to over fitting the recognition model.