# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 4 Results and Analysis
The second model, Leaky ReLU, achieved remarkable outcomes with a recall of 98.9%, accuracy and F1-Score of 98.7%, precision of 98.5%, and an AUC of 0.99. ReLU achieved an impressive performance in the third iteration, with a recall of 98.7%, an accuracy of 98.5%, an F1-Score of 98.4%, a precision of 98.2%, and an AUC of 0.99. Similarly, the Tanh function achieved a recall rate of 97%, an accuracy rate of 96.8%, an F1-Score of 96.7%, a precision rate of 96.5%, and an AUC of 0.97. The sigmoid had the lowest performance compared to all other activation functions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Activation Function** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC Score** \\ \hline ReLU & 98.5 & 98.2 & 98.7 & 98.4 & 0.99 \\ \hline Leaky ReLU & 98.7 & 98.5 & 98.9 & 98.7 & 0.99 \\ \hline Sigmoid & 95.4 & 95 & 95.6 & 95.3 & 0.96 \\ \hline Tanh & 96.8 & 96.5 & 97 & 96.7 & 0.97 \\ \hline Swish & 98.9 & 98.6 & 99 & 98.8 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different activation functions using various parameters. Figure 5: Plant village dataset comparison between the proposed (BERT-ResNet-PSO) approach performance in different numbers of epochs. Figure 6 shows the performance metrics of various activation functions. Swish emerged as the most effective performer, surpassing Lake ReLU, ReLU, Tanh, and Sigmoid in terms of precision, recall, accuracy, F1-Score, and AUC scores. Table 5 presents the plant village dataset comparison between the proposed (BERT-ResNet-PSO) approach performance on different classes. The healthy class achieved exceptional performance, with a recall rate of 99.4%, accuracy of 99.2%, F1-Score of 99.2%, precision of 99%, and AUC value of 0.99. The second class, Curl Virus, achieved notable outcomes with a recall of 99%, accuracy of 98.8%, F1-Score of 98.7%, precision of 98.5%, and an AUC of 0.99. Likewise, Bacterial Blight achieved a recall of 98.9%, accuracy and F1-Score of 98.7%, precision of 98.5%, and an AUC of 0.99. Fusarium Wilt achieved the lowest performance, achieving a recall of 99.4%, an accuracy and F1-Score of 99.2%, a precision of 99%, and an AUC of 0.98. A comparison of the performance of the suggested (BERT-ResNet-PSO) technique on different classes in the Plant Village dataset is shown in Figure 7. It shows the highest values for accuracy, precision, recall, and F1-Score in the healthy class, followed by the curl  
Figure 6: Plant village dataset comparison between the proposed (BERT-ResNet-PSO) approach on different activation functions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC Score** \\ \hline Healthy & 99.2 & 99 & 99.4 & 99.2 & 0.99 \\ \hline Bacterial Blight & 98.7 & 98.5 & 98.9 & 98.7 & 0.99 \\ \hline Fusarium Wilt & 98.3 & 98 & 98.6 & 98.3 & 0.98 \\ \hline Curl Virus & 98.8 & 98.5 & 99 & 98.7 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different classes using various parameters. virus, bacterial blight, and Fusariumium Wilt. Healthy, curl virus and bacterial blight exhibit the maximum AUC, whereas Fusariumium Wilt demonstrates the lowest AUC. Table 6 shows a comparison of dataset performance between the proposed (BERT-ResNet-PSO) segmentation model and existing models. Relative to the Plant Village dataset, both Kaggle Dataset 1 and Kaggle Dataset 2 saw a measure of increased performance when using the BERT-ResNet-PSO approach. The results obtained using the BERT-ResNet-PSO model are superior to the performances of all the other models on different datasets. The model performed with an IoU score of 97.1, a coefficient of a few percentiles, and a mAP score of just around 97% on Kaggle Dataset 2. On the same dataset, it performed a mild IoU of around 90%, indicating it can very well handle complex classification and segmentation tasks.