the acquisition and transmission of citrus images on the mobile operating platform/UAV and realize the accurate recognition of citrus targets for the fine management and yield estimation of orchards. The traditional machine learning method [4; 5] is not ideal for target recognition, and neither speed nor precision can meet the actual needs. In 2012, the proposal of convolutional neural network [6] promoted the upsurge of deep learning, and many target detection models [7] were proposed. At present, target detection algorithms can divide into two categories. One is based on anchor-free type; the main methods are FCOS [8], Centernet [9], etc., instead of setting anchor boxes artificially, objects are detected by point regression. The other is based on anchor-based, which is more used, such as YOLO [10; 11], SSD [12], etc., by setting anchor boxes with different sizes and proportions for each pixel. Specifically, the anchor-based target detection algorithm takes the anchor as the detection starting point, corrects the position and category of the anchor, and obtains the results; this kind of algorithm needs to manually set more parameters, which can more effectively get the prior knowledge of the data set and make the model converge faster. The anchor-free target detection algorithm uses anchor points and key points to pair the objects to be detected; this method reduces the parameters, simplifies the calculation process of the algorithm, and can achieve good results in speed and accuracy. In the detection task in the agricultural field, [PERSON] et al. [13] used the transfer learning method to fine-tune the weight of pre-training and detect the fruit, this method takes into account the detection accuracy and recall, but the model training time is long; [PERSON] et al. [14] used the improved YOLOv3 detection algorithm to process high-resolution image data in real-time, which can quickly detect fruits in orchards; [PERSON] et al. [15] proposed an unsupervised learning method, which uses the target detection algorithm in deep learning to learn plane features, which solves the problem of slow traditional recognition and matching methods; [PERSON] et al. [16] extracted and fused multi-scale features from citrus images to obtain high detection accuracy, but added multi-stage complex search strategy will reduce the detection speed of the model. With the update of hardware devices, many high-performance sensors are widely used [17; 18]. In some specific scenarios, these low-power and portable devices may play an important role, to obtain crop information accurately and timely. [PERSON] et al. [19] used UAV low altitude remote sensing technology to obtain crop images with different resolutions, summarized the research and application of crop growth trend, yield estimation, pest, and forage monitoring, and achieved good results; [PERSON] et al. [20] proposed to transform the model based on color space, segment the foreground (crop) and background (soil background) of UAV image, obtain the classified binary image of the image, and then extract the plant number information of corn seedling image by skeleton extraction algorithm. Some studies have deployed target detection algorithms to edge computing devices to complete different tasks [21; 22; 23]. In orchard scenarios, accurate real-time detection or classification goals effectively judge crop health and yield-related management. [PERSON] et al. [24] implemented the YOLOv3-Tiny target detection algorithm on different embedded platforms. The proposed edge segment solution can deploy on unmanned ground vehicles, and the apples in the orchard can be detected and counted in real-time with a detection accuracy of 83.64%. [PERSON] et al. [25] trained six models by adjusting the number of channels in different depths and widths of the target detection model to detect weeds on Jetson TX2 to reduce damage to surrounding plants. We presented a citrus fruit recognition method based on a deep learning target detection model, mobile data platform, and edge computing device.