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In order to solve the problem of low accuracy and slow speed of identification of rice planthopper caused by incomplete insect image in image acquisition process, a rice planthopper identification and classification method with incomplete insect images based on dictionary learning and single shot multibox detector (SSD) was proposed. Firstly, the field insect image acquisition device was used to acquire rice planthopper images and a small image set was built by these images. Then, single-inspect images were obtained by thresholding of the collected images of rice insects. Single-insect images were divided into blocks to obtain a mixed sub-image blocks with background information and feature information. The sub-image blocks were used as dictionary atoms to construct an over-complete dictionary, and this initial dictionary was optimized and updated immediately. The updated over-complete dictionary was trained as the training set of the SSD algorithm to obtain the training model. Finally, the collected incomplete insect images were tested on the obtained training models, and results were compared with back propagation neural network (BPNN), support vector machines (SVM) and sparse representation. Experimental results showed that the research on the identification and classification method with incomplete images based on dictionary learning and SSD can identify and classify rice planthopper accurately and quickly. Classification speed was 22f/s, the recognition accuracy was 89.3%. Hence, the method proposed can provide effective information and technical support for the supervision, early warning and control of rice planthoppers.