## 3 Result and Discussion
### Comparative Analysis of Model Performance  
In this section, we present a thorough comparison between the proposed hybrid model and several well-established baseline models, including ResNet50, VGG16, MobileNetV2, and MobileNetV2-LSTM. ResNet50 is a deep residual network known for its high accuracy in image classification tasks, utilizing skip connections to mitigate the vanishing gradient problem in deep networks [44]. VGG16 is a convolutional network that employs a deep architecture with small convolutional filters, known for its simplicity and effectiveness in image recognition [45]. MobileNetV2 is a lightweight model optimized for mobile and embedded vision applications, using depth-wise separable convolutions to reduce the number of parameters and computational costs. MobileNetV2-LSTM is another model validated in this research. This method was successfully applied in research [15] to detect the Coronavirus using X-ray images, showing promising results compared to other methods. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **ROC-AUC** \\ \hline Bacterial spot & 0.9937 & 0.9689 & 0.9635 & 0.9662 & 0.9801 \\ Early blight & 0.9924 & 0.9577 & 0.9484 & 0.9530 & 0.9723 \\ Late blight & 0.9958 & 0.9896 & 0.9693 & 0.9793 & 0.9841 \\ Leaf Mold & 0.9989 & 0.9915 & 0.9971 & 0.9943 & 0.9981 \\ Septoria leaf spot & 0.9913 & 0.9452 & 0.9637 & 0.9544 & 0.9789 \\ Spider mites & 0.9968 & 0.9860 & 0.9591 & 0.9724 & 0.9791 \\ Target spot & 0.9955 & 0.9649 & 0.9607 & 0.9628 & 0.9792 \\ Tomato yellow leaf & 0.9989 & 0.9922 & 0.9922 & 0.9922 & 0.9958 \\ Tomato mosaic virus & 0.9979 & 0.9853 & 0.9853 & 0.9853 & 0.9921 \\ Healthy & 0.9984 & 0.9922 & 0.9922 & 0.9922 & 0.9956 \\ Powdery mildew & 0.9971 & 0.9603 & 0.9528 & 0.9565 & 0.9757 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics summarization for the proposed model. Similarly, it was used in [17] in the agricultural field, combining CNN with LSTM to detect foliar disease in apple trees. This method was chosen for comparison to the proposed model because of its promising results in these papers and its low complexity, aligning with this research's goal of creating a lightweight and effective model. MobileNetV2-LSTM extends MobileNetV2 by integrating LSTM units, which enables the model to capture temporal dependencies in sequential data. By evaluating the MobileNetV2-LSTM model, the relative strengths and weaknesses of using LSTM versus CfC in enhancing the classification capabilities for tomato leaf disease detection can be determined. This comparison highlights the potential benefits of the CfC approach, providing a comprehensive assessment of the new method's effectiveness against established techniques. Each of these baseline models was configured and trained with the same hyperparameters as the proposed model to ensure a fair and consistent evaluation. This comparative analysis aims to highlight the strengths and weaknesses of the proposed model in relation to these well-established architectures, thereby validating its performance and robustness in detecting tomato leaf diseases. Figure 10 shows the training and validation results' comparison of the five models. VGG-16 and ResNet-50 both show strong performance, with VGG-16 reaching about 99% training accuracy and its validation accuracy fluctuating but generally remaining high, demonstrating reasonable generalization with occasional drops. ResNet-50 achieves around 99.3% training accuracy but has occasional sharp declines in validation accuracy, suggesting possible overfitting. MobileNetV2 starts lower but reaches an approximately 98.9% training accuracy with a higher and more stable validation accuracy compared to the two above. Figure 9: ROC curves of the proposed model show the performance of the model across various disease classes with corresponding AUC values. The hybrid models show notable improvements.