Abstract: In order to achieve a rapid, accurate and non-destructive detection of citrus pest infestation levels of the fruit trees, a real-time citrus pest detection system based on deep convolutional neural network was designed and developed in this study. The system was composed of a perception layer, a network layer and an application layer. The perception layer was responsible for the collection and identification of pest image data; the network layer was responsible for the data encoding, authentication and transmission between the detection instrument and the cloud server, and between the cloud server and the client; the application layer calculated the degree of damage resulted from pests, based on the number of pests in the target image, then the Beidou module was introduced to obtain the location information of the sampling points, and finally a visual pest heat map was generated. In order to obtain a pest recognition model suitable for the computing requirements of embedded devices, MoblieNet was preferred as the pest image feature extraction network. The regional candidate network generated the preliminary position candidate frame of the pests, and Faster Region Convolutional Neural Networks (Faster R-CNN) realized the classification and positioning of the candidate frame. The results showed that, compared with VGG16 and GoogleNet feature extraction network, MobileNet had the smallest parameter amount, only 15.147 M, and the Mean Average Precision (mAP) and Accuracy (ACC) indicators were 86.40% and 91.07%, respectively. Although the mAP and ACC of MobileNet were lower than VGG16 and higher than GoogleNet, the average time of MobileNet to process an image was 286 ms, which was much less than VGG16 (679 ms) and GoogleNet (459 ms). Considering comprehensively, MobileNet was used as the feature extraction network of the citrus pest detection model in this study. According to the detection effect of the two citrus pests, spider mite and aphids, the recognition rates were both high, reaching 91.0% and 89.0%, respectively, indicating that the sample features were selected correctly. From the perspective of counting accuracy, spider mite was 90.1%, and that of aphids was 43.8%. The main reason was that aphids were dense and obscure each other, and there was overlap so that it was difficult to label all pests when labeling. During the model training process, some aphids samples became negative samples and the accuracy rate was reduced. In addition, the pest distribution heat map had a small error, and directly displayed the degree of damage in different target point. The system realized the accurate identification and positioning of citrus pests, and provided an accurate information services for pesticide spraying operations.