Automatic identification and count of rice light-trapped pests is a common and important pest forecasting method in paddy fields. However, most of the light-trapped pests are unnecessary to be monitored and must be removed. This manual method is time-consuming and fatiguing with a low accuracy rate. We developed an automatic method for identifying rice light-trapped pests based on the images. Firstly, we divided the images into several groups according to their morphological features. Each group has three classifications: the back-side image of a pest, the abdomen-side image of this pest, and non-forecasting pest image similar to this pest. Then, thirty-one features including the color, shape and texture features were extracted from each insect image. Finally, three support vector machine classifiers with posterior probabilities were used to train and test the three groups of insects, respectively. In the results, the back-side image and the abdomen-side image of a pest were considered as the same species. We achieved a 97.4% accuracy rate in the three species of rice light-trapped pests.