A tomato leaf disease detection model based on the improved CenterNet algorithm was proposed in order to address the false detection and missed detection phenomena in traditional tomato leaf disease detection. A feature fusion module that integrated the attention mechanism was constructed in order to enhance the model's cross-scale feature fusion capability. The multi-branch convolutional module RFB was added to the backbone network in order to expand the receptive field and enhance the ability to extract target features. The pyramid convolution PyConv was introduced into the backbone network to enhance the extraction of multi-scale features by calculating receptive fields of different scales and reduce information loss. Pruning optimization strategies were designed in order to reduce the impact of introducing modules on the number of model parameters and computational load. The test results showed that the accuracy rate, recall rate, mAP₅₀ and mAP_50:95 of the improved model reached 96.3%, 80.2%, 91.4% and 78.7% respectively. The proposed model can effectively improve the accuracy of tomato leaf disease detection, and the model has good generalization.