# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 4 Results and Analysis
Overall, the results show the little model outperforms all evaluated metrics better than the baseline and previous models. Last but not least, BERT-ResNet-PSO also demonstrates the highest overall performance in terms of AUC score, measuring the average quality of the model given across all classification thresholds by 0.99. Next is EfficientNet, with a score of 0.97 and both ResNet-50 and DenseNet scored 0.96. Meanwhile, InceptionV3 and VGG16 score 0.95, while the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC Score** \\ \hline BERT-ResNet-PSO & 98.5 & 98.2 & 98.7 & 98.4 & 0.99 \\ \hline CNN (Baseline Model) & 92 & 91.5 & 92.3 & 91.9 & 0.94 \\ \hline ResNet-50 & 95.2 & 94.8 & 95.5 & 95.1 & 0.96 \\ \hline VGG16 & 93.7 & 93.1 & 94 & 93.5 & 0.95 \\ \hline InceptionV3 & 94.5 & 94 & 94.8 & 94.4 & 0.95 \\ \hline EfficientNet & 96 & 95.6 & 96.3 & 95.9 & 0.97 \\ \hline DenseNet & 95.8 & 95.3 & 96 & 95.6 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different models using various parameters. CNN baseline ends with 0.94 in the table. Table 2 shows that the BERT-ResNet-PSO model exhibited better performance than the other models for all the metrics. Figure 4 illustrates the comparison of the plant village dataset using various deep learning models, including the proposed (BERT-ResNet-PSO) model. It is evident that the BERT-ResNet-PSO model outperformed the other models in terms of precision, accuracy, recall, F1-Score, and AUC values. Following closely behind were the EfficientNet model, DenseNet, ResNet-50, InceptionV3 model, VGG16 model, and the CNN (Baseline Model). Table 3 displays the dataset showcasing the performance of the proposed BERT-ResNet-PSO approach across various epochs. Based on the data in Table 3, at the 200 th epoch, the highest recall rate achieved is 98.7%, followed by an accuracy rate of 98.5%, an F1-Score of 98.4%, and a precision rate of 98.2%. Additionally, the AUC value is 0.99. Similarly, at the 150 th epoch, a recall rate of 98.3% is achieved, along with an accuracy and F1-Score of 98.1%, a precision of 97.9%, and an AUC of 0.99. After reaching the 100 th epoch, an impressive recall rate of 97.2% was achieved, along with an accuracy of 97%, an F1-Score of 96.9%, a precision of 96.7%, and an AUC of 0.98. In addition, the 75 th, 50 th, 30 th, and 20 th achieved the highest recall values, with accuracy, F1-Score, precision, and AUC values. Finally, the 10 th epoch had the lowest performance compared to the others. Figure 4: Plant village dataset compassion between proposed (BERT-ResNet-PSO) with different DL models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Epochs** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC Score** \\ \hline
10 & 85.2 & 84.8 & 85.5 & 85.1 & 0.88 \\ \hline
20 & 89.7 & 89.2 & 89.9 & 89.5 & 0.92 \\ \hline
30 & 92.3 & 91.8 & 92.5 & 92.1 & 0.94 \\ \hline
50 & 94.8 & 94.4 & 95 & 94.7 & 0.96 \\ \hline
75 & 96.2 & 95.9 & 96.4 & 96.1 & 0.97 \\ \hline
100 & 97 & 96.7 & 97.2 & 96.9 & 0.98 \\ \hline
150 & 98.1 & 97.9 & 98.3 & 98.1 & 0.99 \\ \hline
200 & 98.5 & 98.2 & 98.7 & 98.4 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different epochs using various parameters. Figure 5 shows the performance of the proposed (BERT-ResNet-PSO) approach over different epochs. It is evident that there is a steady increase in accuracy, precision, recall, F1-Score, and AUC values from the 10 th epoch to the 200 th epoch. Optimal parameters are achieved at the 200 th epoch, while the least favorable parameters are obtained at the 10 th epoch. The proposed (BERT-ResNet-PSO) approach with various activation functions is shown in Table 4. The Swish activation function achieved exceptional performance, with a recall rate of 99%, an accuracy of 98.9%, an F1-Score of 98.8%, a precision of 98.6%, and a high AUC value of 0.99.