Aiming at the different sizes of the disease manifestations of citrus leaves, the model of improved CenterNet is proposed to solve the problems of missing detection, false detection and low accuracy in the detection process. The feature enhancement improved atrous spatial pyramid pooling (IASPP) module was introduced into a series of residual structures of the first two residual layers of the feature extraction network RestNet50 to expand the shallow receptive field, obtain more detailed information of small target leaf diseases, and enhance the significance of shallow features. The bidirectional feature pyramid network (BiFPN) module was introduced to effectively integrate the shallow and deep leaf disease information of the feature extraction network. In order to improve the overall detection effect, the multi-scale channel attention module (MS-CAM) was introduced. The trained model was used to detect citrus disease leaves. The experimental results show that, compared with the original model CenterNet, the R-value of the proposed model is increased by 8.32%, mAP is increased by 4.53%, and AP0.5:0.95 is increased by 27.3%. It can achieve accurate detection of small target, medium target and large target leaf diseases in citrus planting.