An improved YOLO11 algorithm integrating multiple modules was proposed to solve the problems of uneven distribution of categories， difficulties in extracting features of small target， and insufficient accuracy in detecting tomato leaf diseases in complex environments.Firstly， the adaptive threshold focal loss （ATFL） was used to replace the loss of traditional cross-entropy.The process of training was optimized by implementing a dynamic weight allocation strategy to address the issue of imbalanced samples of diseases， enhance the ability of model to learn features for disease categories with low-frequency and improve the overall accuracy of detection.Secondly， the hybrid pooling attention module （HPA） was introduced to replace the ordinary convolution in the C3k2 module， and a parallel pooling branch was designed.The ability of extracting spatial feature of small target diseases was strengthened by combining with the group feature recalibration technique to enhance the ability of antiocclusion detection in complex environments.Finally， the normalization in the Conv structure was replaced with re-parameterized batch normalization （RepBN）， and a learnable adjustment parameter was introduced to reconstruct the process of normalization to stabilize the feature distribution at the stage of training， accelerate the convergence of model， and enhance the performance of generalization.The results showed that the improved YOLO11 model had excellent performance on the dataset of tomato leaf diseases， with increases of 5.8， 4.3， 2.3， and 1.2 percentage points in the precision， recall， mAP@0.5， and mAP@0.5：0.95 evaluation metrics compared with that of the YOLO11 base model， respectively.It had good performance on detecting areas of small target diseases and scenarios of dense occlusion in particular.It is indicated that the improved YOLO11 algorithm effectively improves the performance of the model in detecting tomato leaf diseases， achieving a synchronous improvement in the accuracy and robustness of detecting tomato leaf diseases.