## 4 Discussion  
The performance differences of optimizers are significant. The unsatisfactory performance of SGD is due to its stochastic gradient descent on the learning rate, where the original learning rate is set to 0.0001, resulting in slow convergence. To verify this, we trained with an initial learning rate of 0.01, and the results are shown in Figure 10. Adagrad makes it difficult for the model to converge due to its dynamically decreasing learning rate. Using a higher initial learning rate can solve this problem, but ultimately, RMSprop outperforms Adagrad under the same conditions. RMSprop was developed to address the rapid learning rate decrease issue in Adagrad, resulting in the fastest fitting speed and highest accuracy. The idea behind Adam is to set the initial learning rate to a larger value and dynamically decrease it as the number of iterations increases, achieving a balance between efficiency and effectiveness. Therefore, it also achieves good results. Considering all factors, we chose RMSprop as the optimizer for LBFNet. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Size** & **Accuracy** & **Loss** & **\(F1\)-Score** & **Recall** & **Precision** \\ \hline LBFNet & 6.85 MB & 0.9906 & 0.0408 & 0.98 & 0.98 & 0.98 \\ \hline pruned\_quantized\_model & 3.46 MB & 0.9766 & 0.0712 & 0.97 & 0.97 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison between the original model and quantitative pruning model. Figure 9: The comparison of the accuracy of different models on LBFtomato. From Figures 11 and 12, it can be seen that when the learning rate is set to 0.0001, LBFNet achieves the best fitting speed, highest accuracy, and smallest loss, and the expected effect is achieved. Therefore, 0.0001 is chosen as the learning rate value of LBFNet. Excessive learning rate in tomato leaf disease identification cannot support the actual production needs for RMSprop optimizer. The confusion matrix is a situation analysis table in machine learning that summarizes the prediction results of the classification model. Figures 13-19 show the confusion matrix of the seven models, and it can be seen that LBFNet has good results for diseases with both large and small differences between classes. Table 7 shows that for LBFNet, the classification and recognition effects of all types of diseases meet the criteria of the actual situation, with all indicators close to 1. Even for mosaic_virusy, which has the least samples, it achieves 94% high accuracy, fully compatible with the needs of agricultural production, and there are no extreme cases. Figure 11: Accuracy comparison of different learning rates. Figure 10: The accuracy graph using the SGD optimizer with a learning rate of 0.01. Figure 12: Loss comparison of different learning rates. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **F1-Score** & **Recall** & **Precision** & **Image Numbers** \\ \hline Bacterial\_spot & 0.96 & 0.98 & 0.97 & 340 \\ Early\_blight & 0.97 & 0.96 & 0.97 & 200 \\ healthy & 0.98 & 0.98 & 0.98 & 381 \\ Late\_blight & 0.99 & 0.99 & 0.99 & 192 \\ Leaf\_Mold & 0.99 & 0.99 & 0.99 & 355 \\ Septoria\_leaf\_spot & 0.98 & 0.99 & 0.99 & 335 \\ Spider\_mites & 0.99 & 0.96 & 0.99 & 284 \\ Target\_Spot & 0.99 & 0.98 & 0.99 & 258 \\ mosaic\_virus & 0.94 & 1.0 & 0.97 & 74 \\ yellow\_Leaf\_Curl\_Virus & 0.99 & 0.99 & 0.99 & 254 \\ accuracy & & & 0.98 & 2673 \\ macro avg & 0.98 & 0.98 & 0.98 & 2673 \\ weighted avg & 0.98 & 0.98 & 0.98 & 2673 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The accuracy, precision, recall, and \(F1\) score for the LBFNet. Figure 13: Confusion matrix of LBFNet: 0: ‘Bacterial_spot’,1: ‘Early_blight’, 2: ‘healthy’, 3: ‘Late_blight’, 4: ‘Leaf_Mold’, 5: ‘Septoria_leaf_spot’, 6: ‘Spider_mites’, 7: ‘Target_Spot’, 8: ‘mosaic_virus’, 9: ‘yellow_Leaf_Curl_Virus’. Figure 16: Confusion matrix of MobileNet. Figure 14: Confusion matrix of vit-transformer. Figure 15: Confusion matrix of GoogleNet. Figure 19: Confusion matrix of ConvNeXt. Figure 17: Confusion matrix of VGG16.