## 3 Results
### Model Performance Comparison
#### 3.2.3 Comparative Analysis Result  
To explore the effect of data increment on model accuracy, different models were trained on PlantVillage and LBFtomato datasets, and the results are presented in Tables 4 and 5. It can be seen that the performance of all models improved on the LBFtomato dataset, proving that balancing positive and negative samples optimizes the dataset to improve model performance. Meanwhile, LBFNet achieves excellent results on different datasets, and convergence speed, and model accuracy. Although lower than the two large models, ConvNeXth and vit-transformer, the training time is Two hundred times lower than these two models and maintains the minimum number of parameters, and the accuracy rate is only 0.01 compared to the large models, while the training time is the shortest. In addition, for the problems of large parameters, long training time, and low accuracy of traditional models such as VGG and ResNet, LBFNet is a good solution that can be flexibly applied to tomato leaf disease identification in modern agriculture.  
As shown in Figure 9, GoogleNert and MobileNet, as early lightweight models, have fewer parameters and simple structures, but the same training time increases accordingly and cannot achieve a complete fit after 100 rounds, while VGG16, although simple but with a huge number of parameters and large fluctuations, also does not achieve a fit, and ResNet has the disadvantage of test time, while vit-transformer, ConvNeXt, and LBFNet can reach saturation within a very short number of rounds, but vit-transformer and ConvNeXt have the disadvantages of a large number of parameters, complex structure and long training time of the model, while LBFNet has an accuracy of 0.99. The minimum number of parameters and very simple structure can effectively solve the current problems of low accuracy, large model, long time, and difficulty to deploy on mobile devices for tomato leaf disease recognition models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **Parameters** & **Train Time/s** & **Test Time/s** & **F1-Score** & **Recall** & **Precision** \\ \hline Resnet50 [38] & 0.9482 & 0.1579 & 23,608,202 & 28,377 & 0.51 & 0.92 & 0.91 & 0.92 \\ Vgg16 [39] & 0.9590 & 0.0891 & 165,758,794 & 41,577 & 0.23 & 0.96 & 0.96 & 0.96 \\ Mobilenet [18] & 0.9492 & 0.1449 & 2,279,714 & 10,142 & 0.40 & 0.90 & 0.91 & 0.91 \\ Googlenet [40] & 0.8633 & 0.3947 & 10,360,590 & 7857 & 0.32 & 0.87 & 0.87 & 0.87 \\ LBFNet & 0.9906 & 0.0408 & 897,188 & 966 & 0.21 & 0.98 & 0.98 & 0.98 \\ vit-transformer [41] & 1.0 & 0.012 & 85,806,346 & 365,320 & 0.28 & 1.0 & 0.97 & 0.98 \\ ConvNeXt [42] & 0.9884 & 0.071 & 27,827,818 & 197,320 & 0.42 & 0.99 & 0.99 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: LBFtomato Model Cluster Evaluation Metrics. Time taken to train the model for 100 epochs; Test time: The forecast time for a single image.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **Parameters** & **Train Time/s** & **Test Time/s** & **F1-Score** & **Recall** & **Precision** \\ \hline Resnet50 & 0.8965 & 0.3025 & 23,608,202 & 27,837 & 0.54 & 0.81 & 0.79 & 0.80 \\ Vgg16 & 0.8175 & 0.5938 & 165,758,794 & 41,926 & 0.25 & 0.80 & 0.79 & 0.77 \\ Mobilenet & 0.7920 & 0.5924 & 2,279,714 & 15,858 & 0.45 & 0.77 & 0.79 & 0.80 \\ Googlenet & 0.8281 & 0.5588 & 10,360,590 & 7172 & 0.36 & 0.82 & 0.84 & 0.82 \\ LBFNet & 0.9756 & 0.2696 & 897,188 & 1420 & 0.23 & 0.97 & 0.98 & 0.98 \\ vit-transformer & 0.9943 & 0.015 & 85,806,346 & 412,702 & 0.41 & 0.99 & 0.98 & 0.99 \\ ConvNeXt & 0.978 & 0.089 & 27,827,818 & 277,A56 & 0.52 & 0.97 & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: PlantVillage Model Cluster Evaluation Metrics.