# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 4 Results and Analysis
Table 7 presents the results of 5-fold cross-validation conducted on the proposed BERT-ResNet-PSO model across three datasets: PlantVillage, Kaggle Dataset 1, and Kaggle Dataset 2. The model demonstrates high consistency and stability, with a very narrow variance between folds, indicating strong generalization capability. On the PlantVillage dataset, the model achieves a mean accuracy of 98.5%, with individual fold accuracies ranging from 98.4% to 98.6%. Similar consistency is seen in Kaggle Dataset 1 and 2, with mean accuracies of 98.3% and 98.0%, respectively as seen in Table 8. This robustness across folds confirms that the model is not overfitting to specific data splits and maintains reliable performance across different subsets of the data. The cross-validation results further reinforce the model's reliability and adaptability, making it well-suited for deployment in real-world agricultural disease classification tasks where generalization is critical. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Approach** & **Dataset** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC Score** & **Author(s)** \\ \hline \multirow{4}{*}{BERT-ResNet-PSO} & PlantVillage & 99 & 98.9 & 98.8 & 98.85 & 0.98 & Proposed \\ \cline{2-7}  & Kaggle & \multirow{2}{*}{98.8} & \multirow{2}{*}{98.7} & \multirow{2}{*}{98.6} & \multirow{2}{*}{98.65} & \multirow{2}{*}{0.978} & \multirow{2}{*}{Proposed} \\  & Dataset 1 & & & & & & \\ \cline{2-7}  & Kaggle & \multirow{2}{*}{98.5} & \multirow{2}{*}{98.3} & \multirow{2}{*}{98.2} & \multirow{2}{*}{98.25} & \multirow{2}{*}{0.99} & \multirow{2}{*}{Proposed} \\  & Dataset 2 & & & & & & \\ \hline ResNet-50 & PlantVillage & 96.5 & 96.1 & 96.7 & 96.4 & 0.97 & [PERSON] et al. [29] \\ \hline Hybrid & Kaggle & \multirow{2}{*}{95.8} & \multirow{2}{*}{95.3} & \multirow{2}{*}{96} & \multirow{2}{*}{95.6} & \multirow{2}{*}{0.96} & \multirow{2}{*}{[PERSON] et al. [30]} \\ CNN-RNN & Dataset 2 & & & & & & \\ \hline DenseNet-201 & PlantVillage & 97.5 & 97 & 97.8 & 97.4 & 0.97 & [PERSON] and [PERSON] [26] \\ \hline InceptionV4 & Kaggle & \multirow{2}{*}{96.9} & \multirow{2}{*}{96.5} & \multirow{2}{*}{97.1} & \multirow{2}{*}{96.8} & \multirow{2}{*}{0.96} & [PERSON] and [PERSON] [31] \\  & Dataset 1 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Different dataset comparisons between proposed (BERT-ResNet-PSO) approach classification performance and other existing performance. An analysis of the segmentation performance between the suggested methodologies (BERT-ResNet-PSO) and other existing models using various datasets is presented in Figure 10. Based on the graph, the recall percentage is consistently the highest, followed by accuracy, F1-Score, and precision in all cases. All models yield a favorable AUC value. The proposed BERT-ResNet-PSO technique demonstrated superior performance compared to other existing models across various datasets. It outperformed EfficientNet-B7 (PlantVillage), DenseNet-201 (PlantVillage), ResNet-50 + SVM model (Kaggle Dataset 1), InceptionV4 model (Kaggle Dataset 1), ViT (PlantVillage), ResNet-50 (PlantVillage), MobileNetV2 (Kaggle Dataset 1), Hybrid CNN-RNN (Kaggle Dataset 2), and VGG19 (Kaggle Dataset 2), respectively. Table 9 presents a comparative analysis of the proposed BERT-ResNet-PSO model against its counterparts using traditional optimizers such as AdamW and SGD across  
Figure 10: Different dataset comparisons between proposed (BERT-ResNet-PSO) approach classification performance and other existing performance.