# A Study on Tomato Disease and Pest Detection Method
## 6 Discussion  
Based on the experimental results of this study, the following conclusions can be drawn:  
First, by replacing the backbone network with EfficientViT, we found that the number of parameters in the model was significantly reduced while the accuracy was slightly improved. Specifically, [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 increased by 1.1% and 0.6%, respectively, while the number of floating-point operations per second performed by the model decreased by 1.2 GFLOPs, a 28% decrease, and the number of parameters decreased by 0.4 M, a 21% decrease. This indicates that the EfficientViT backbone network proposed in this paper has significant effectiveness in tomato disease and pest detection tasks.  
Secondly, we found that improving the original nearest neighbor interpolation upsampling to CARAFE upsampling can improve the model's detection accuracy with a slight increase in model size and computational complexity. Specifically, [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 increased by 1.3% and 1.0%, respectively, while the increase in model floating-point operations and number of parameters was within an acceptable range. This indicates that the CARAFE upsampling operator proposed in this paper is effective in improving model accuracy in tomato disease and pest detection tasks.  
Thirdly, we found that improving the original CIoU Loss to a WIoU Loss can improve model detection accuracy without affecting model volume or detection speed. Specifically, [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 increased by 0.4% and 0.6%, respectively. This indicates that the WIoU Loss proposed in this paper has significant effectiveness in tomato disease and pest detection tasks.  
Additionally, we found that the proposed improved algorithm, YOLOv5n-VCW, has the highest detection accuracy while ensuring model lightness. Compared with other mainstream object detection algorithms, the proposed algorithm in this paper can maintain high detection accuracy even with a decrease in the number of model parameters and computational complexity, making it highly applicable.  
Finally, we found that the proposed YOLOv5n-VCW algorithm has significant advantages in resource-limited situations. Compared with other algorithms, this algorithm has the highest detection accuracy with a smaller number of model parameters and computational complexity. This indicates that the YOLOv5n-VCW algorithm proposed in this paper is significantly effective and superior for tomato disease and pest detection tasks.  
However, our study has some limitations. First, this study conducted experiments only for tomato pest detection tasks and did not consider other types of object detection tasks. Second, this study was only based on a single dataset, and we did not consider the  
Figure 21: YOLOv5n-VCW predictive images.  
differences and influences between different datasets. Finally, the lighting and occlusion effects in the experiment were realized based on data enhancement, which may differ from the real scene, so this is a theoretical approach. It is also a direction for future research.  
In conclusion, the results of this study show that the proposed improved algorithm theoretically has significant effectiveness and superiority in tomato disease and pest detection tasks. These results provide useful clues for future research and feasible solutions for practical applications. Future research can go a step further to explore how to apply the proposed improved algorithm to other types of object detection tasks and conduct experimental verification, as well as how to improve the real-time performance of the model and better deal with the differences and influences between different datasets.