On the basis of the differences in physiology and physics of rice seed with different aging time, the paper proposes a fast and nondestructive method which is based on infrared thermal imaging technology and generalized regression neural network to detect the germination rate of rice seed. This method solves the problems of long experimental period, complex operations and other disadvantages of the traditional method which is used to detect germination rate. When the temperature is 45 ℃ and humidity is 90%, the rice seeds are aged for 0, 1, 2, 3, 4, 5, 6 and 7 d respectively to get rice seeds of different germination rate. The data of 144 groups was extracted from the germ of rice seed. This data was divided into two groups randomly: the calibration set was 96 groups and the prediction set was 48 groups. Through analyzing and comparing the differences of infrared thermal image of rice seeds of different aging days, the relationship in physics and physiology between germination rate of rice seed and infrared thermal images was revealed. The infrared prediction model for germination rate of rice seed was established by combining partial least squares algorithm, Back Propagationneural network and General regression neural network. The result shows that the optimal germination rate model is built with GRNN. In this model, the correlation coefficient (RC) and standard deviation (SEC) of calibration sets are 0.932 0 and 2.056 0. At the same time, the correlation coefficient (RP) and standard deviation (SEP) of prediction sets are 0.900 3 and 4.101 2. The relevance reaches a higher level and the standard deviation is small. Therefore, the experiment shows that combining infrared thermal imaging technology with GRNN to study germination rate of rice seed is feasible. The model has a higher accuracy in terms of rapid determination of the germination rate of rice seed.