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Accurate prediction of rice pest plays a very important role in ensuring high yield of rice and reducing economic losses. Manual rice pest survey methods in paddy fields are time-consuming. With the developments in computer vision technology and theory, machine learning and deep learning have been applied in automatic identification of agricultural pests, which have greatly improved image classification accuracy of rice pest. Target detection algorithm YOLO v5 was introduced to identify and count Cnaphalocrocis medinalis and Chilo suppressalis on monitoring equipment and traps. Based on the biological habits of C.medinalis and C.suppressalis, a multi-source rice pest images dataset was constructed from the images of adults of C.medinalis and C.suppressalis , which were captured by a self-developed rice pest trap and photo device, triangular traps and light traps. The number of images in the dataset were increased by flipping the image left to right, increasing the image contrast, and flipping the image up and down. The detection performance of different training models on rice pest images captured by triangle traps and monitoring device was compared, and the effects of different training sample sizes on the identification results were compared. Precision, recall, F1 score and average precision were used to evaluate the difference of each model. The models were tested on the rice pest images captured by triangle traps and monitoring device. The results showed that the precision and recall of C.medinalis were 91.67% and 98.30%, respectively, and the F1 score was 94.87%. The precision and recall of C.suppressalis were 93.39% and 98.48%, respectively, and the F1 score was 95.87%. Multi-source rice pest images dataset constructed by different sampling background and equipment can improve the accuracy of rice pest by identification model. The rice pest identification and counting model developed based on YOLO v5 algorithm can achieve high identification accuracy and it can be used to monitor population of C.medinalis and C.suppressalis in the field.