In order to solve the disadvantages such as strong subjectivity, low automation and low efficiency of spores detection in rice blast, an additive intersection kernel support vector machine (IKSVM) based on histogram of oriented gradient feature (HOG feature) was proposed to detect rice blast spores. Firstly, the image acquisition system was used to collect spores images of rice blast disease, and Gamma correction was used to adjust the contrast of the images to suppress noise interference. Secondly, the HOG feature of the spores image was extracted as input vectors and input into the support vector machine to construct the intersection kernel support vector machine classifier. Finally, the rice blast spores classifier was obtained by training. In order to test the comprehensive performance of proposed HOG/IKSVM, the HOG/linear SVM method and the HOG/radial basis function kernel SVM (RBF-SVM) method were used for the comparison test. The test results showed that the detection rate of HOG/IKSVM was 98.2%, which was higher than the 79% of the HOG/linear SVM method. On average detection time, the average detection time of HOG/IKSVM was only 1.1% of the HOG/RBF-SVM method. This method can be used as a rapid and accurate identification method for indoor detection of rice blast.