## 4 Experiment and Evaluations
### Experimental Results
#### 4.4.2 Ablation Experiment  
To thoroughly evaluate the efficiency of the proposed approach, this study performed several ablation experiments to assess the impact of individual components on model performance and further investigate the advantages of the improved algorithm. To ensure the reliability and consistency of the results, all models were trained from scratch without the use of pre-trained weights, eliminating external factors and strengthening the validity of the findings. The outcomes of the ablation experiments are presented in Table 5.  
Figure 10: The effect diagram of the model’s comparative experiment on public datasets. Column (**a**) is the original image of Tea exobasidium blight, Tea red scab, Tea cake, and Tea cloud leaf blight from top to bottom. Columns (**b**,**c**) correspond to the effect of disease detection before and after improvement of the model, respectively. In the experiments, although the precision showed only a slight improvement, other critical performance metrics demonstrated significant enhancements. For instance, the FLOPs were drastically reduced from 57.00 to 28.30, improving efficiency by over two-fold. The FPS increased from 237.40 to 346.40, indicating a significant improvement in real-time performance, enabling the model to process tasks more efficiently. The number of parameters decreased from 19.88 M to 10.30 M, nearly halving the model complexity and reducing storage requirements substantially. Similarly, the model size was reduced from 40.50 M to 20.40 M, further highlighting the advantages of the lightweight design. Overall, compared to the conventional RT-DETR, the proposed improved algorithm not only achieves higher real-time performance but also significantly reduces computational and storage requirements, making it viable for implementation on devices with limited resources. Table 5 illustrates that the improved algorithm significantly surpasses the original one in overall performance. Additionally, to further demonstrate the practical effectiveness of the enhanced model, this study conducted a comparative analysis using specific detection cases, with the results presented in Figure 11. In order to facilitate detailed error analysis of the model, the confusion matrix is specially introduced, as shown in Figure 12.  
The first row illustrates the detection results for red leaf spot. The improved model shows notable improvements in accuracy and precision in bounding boxes compared to the original model, capturing not only the diseased regions more closely but also subtle disease features that were previously undetected, showcasing the model's responsiveness to fine-grained details. In the second row, depicting tea white spot disease detection, the original model misidentified white cracks along the leaf edges as disease, whereas the improved model accurately distinguished diseased and non-diseased regions through more precise feature extraction and exhibited a stronger capability to detect minor disease spots on the leaves. The third row presents results for tea coal disease detection, where the improved model achieves markedly enhanced localization accuracy. Its bounding boxes align more closely with the diseased regions, providing comprehensive coverage of the affected areas and significantly reducing both missed and false detections. The fourth row highlights the detection of tea brown blight disease. The original model exhibited clear omissions in the lower part of the bounding box, failing to fully cover the diseased region. The improved model, with its superior feature extraction and modeling capabilities, successfully addressed this issue, achieving complete coverage of the diseased areas. In summary, the improved RT-DETR model demonstrates significant advancements in real-time performance, lightweight design, and detection capabilities.