# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 4 Results and Analysis
The subsequent rankings are as follows: EfficientNet-B7 (PlantVillage), DenseNet-201 (PlantVillage), ResNet-50 + SVM (Kaggle Dataset 1), ViT (PlantVillage), ResNet-50 (PlantVillage), Inception V3 (Kaggle Dataset 1), Hybrid CNN-RNN (Kaggle Dataset 2), MobileNetV2 (Kaggle Dataset 1), and VGG19 (Kaggle Dataset 2), respectively. The Inception V3, a hybrid CNN-RNN, demonstrated average performance. In addition, the MobileNetV2 (Kaggle Dataset 1) and VGG19 (Kaggle Dataset 2) models exhibited low performance in contrast to the other models. A comparison of classification performance between the BERT-ResNet-PSO model and other existing approaches is shown in Table 7. The performance of the proposed methodologies was superior to all other models. On Kaggle Dataset 1 and Kaggle Dataset 2, the BERT-ResNet-PSO technique surpassed the performance of the PlantVillage dataset. The BERT-ResNet-PSO technique, with Kaggle Dataset 1, achieves a recall and an accuracy of 98.9% and 98.7%, F1-Score is reaching 98.6%, Precision has performed close to the limit of 98.4%, and AUC of.99 On the other hand, Kaggle Dataset 2, when applied in fine-tuning, achieved a great recall rate (98.8%), accuracy rate (98.6%), F1-Score (98.5%) and precision (98.3%) and AUC = 0.99. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Fold 1 (\%)** & **Fold 2 (\%)** & **Fold 3 (\%)** & **Fold 4 (\%)** & **Fold 5 (\%)** & **Mean Accuracy (\%)** \\ \hline PlantVillage & 98.6 & 98.5 & 98.4 & 98.5 & 98.6 & 98.5 \\ \hline Kaggle Dataset 1 & 98.4 & 98.3 & 98.3 & 98.2 & 98.3 & 98.3 \\ \hline Kaggle Dataset 2 & 98.1 & 98.0 & 98.0 & 97.9 & 98.0 & 98.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Five-cross validation for the proposed approach. Figure 9: Different dataset comparisons between the proposed (BERT-ResNet-PSO) approach segmentation performance and other existing performance. Furthermore, significant results with a recall rate 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 are obtained based on the PlantVillage dataset [30]. In their study, [PERSON] et al. [25] employed EfficientNet-B7 (PlantVillage), which achieved impressive results. Their recall was 98%, accuracy 97.8%, F1-Score 97.7%, precision 97.4%, and AUC of 0. [PERSON] and [PERSON] [26] achieved remarkable results using DenseNet-201 (PlantVillage) in their study. Their results were a recall of 97.8%, an accuracy of 97.5%, an F1-Score of 97.4%, a precision of 97% and an AUC (Area Under Curve) equal to 0.97. [PERSON] and [PERSON] [27] use the ResNet-50 + SVM model on Kaggle Dataset 1, and the obtained performance matrix can also be seen next. They achieved 97.2% recall, 97% accuracy, 96.8% F1-Score, 96.6% precision and an AUC value of 0.97 specifically. [PERSON] and [PERSON] [31] demonstrated that the InceptionV4 model achieved better results on Kaggle Dataset 1. The model achieved a recall of 97.1%, accuracy of 96.9%, F1-Score of 96.8%, precision of 96.5%, and AUC:0.96 [PERSON] et al. [28] exploited the model from ViT, PlantVillage dataset to obtain remarkable levels of performance (Table 2). Their recall rate was 97%, accuracy was 96.8%, F1-Score was 96.7%, precision was 96.5%, and AUC the AUC value achieved an AUC = 0.98. [PERSON] et al. [29] achieved improved results on the PlantVillage data set with the ResNet-50 model. The recall rate reached 96.7%, the accuracy reached 96.5%, the F1-Score got to 96.4%, the precision got up to 96.1%, and the AUC is also high, with a score of 0.97. These generally high values show that using all features does not increase our model metrics for this specific filter selection, meaning that this model could have performed better using just these snippets as input data. Additionally, MobileNetV2 (Kaggle Dataset 1), Hybrid CNN-RNN (Kaggle Dataset 2), and VGG19 (Kaggle Dataset 2) exhibited low performance compared to other models.