# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 4 Results and Analysis
On the same Plant Village dataset, the performance of the BERT-ResNet-PSO model is also exceptional, with a coefficient of 98.4%, a mAP score of 97.5% and an IoU of 96.8%. The results presented here exceed the previously benchmarked results in related studies. The other state-of-the-art approaches have also achieved competitive results. For instance, [PERSON] et al. [25] used machine learning techniques on the Plant Village dataset to identify potential gene candidates, and they did it very well. Recently, the EfficientNet B7 in 2023, at roughly 1.5x the size compared to EfficientNet V2, achieved a coefficient of 96.8%, a mAP score of 95.2%, and an IoU score of 94.3%. Similar results were obtained by [PERSON] and [PERSON] [26] using the DenseNet-201 structure, with an overall coefficient of 96.5%, mAP of 95% and IoU of 94%. [PERSON] and [PERSON] [27] employed a ResNet-50 + SVM to Kaggle Dataset 1, achieving a coefficient of 96%, a mAP of 94.5% and an IoU of 93.9%. Similarly, [PERSON] et al. [28] utilized the Vision Transformer (ViT) model on the dataset and obtained a coefficient of 96.2%, mAP 94.3% and IoU 93.5%. Similarly, ideas on how to improve the segmentation models have been contributed by [PERSON] et al. [29] through improvements to ResNet-50. Finally, their model reached a coefficient of 95.6%, mAP of 94% and IoU of 94.2%. However, models such as Inception V3 and hybrid CNN-RNN architectures performed only average. MobileNetV2 and VGG19 were tested on Kaggle Dataset 1 and showed lower performance compared to many contemporary models, based on how good or bad these models performed. Modern approaches are highlighted in these results with  
Figure 7: Plant village dataset comparison between the proposed (BERT-ResNet-PSO) approach and performance on different classes. particular emphasis on the BERT-ResNet-PSO model, which establishes a new benchmark for classification and segmentation tasks. VGG19 models achieved low performances on Kaggle Dataset 1, in contrast to the rest of the models. The performance matrix can be seen in Figure 8.  
Figure 9 illustrates a comparison of the segmentation performance between BERT-ResNet-PSO and other existing models using different datasets. It can be seen that the dice coefficient has the largest percentage, followed by mAP% and IoU%. The BERT-ResNet-PSO model achieved remarkable outcomes on Kaggle Dataset 1, followed by Kaggle Dataset 2 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Dataset** & **IoU (\%)** & **mAP (\%)** & **Dice Coefficient (\%)** & **Author(s)** \\ \hline
**ResNet-50** & PlantVillage & 93.2 & 94 & 95.6 & [PERSON] et al. [29] \\ \hline
**Hybrid CNN-RNN** & Kaggle Dataset 2 & 91.8 & 92.5 & 94.2 & [PERSON] et al. [30] \\ \hline
**DenseNet-201** & PlantVillage & 94 & 95 & 96.5 & [PERSON] and [PERSON] [26] \\ \hline
**InceptionV4** & Kaggle Dataset 1 & 92.5 & 93.8 & 95.2 & [PERSON] and [PERSON] [31] \\ \hline
**VGG19** & Kaggle Dataset 2 & 90.7 & 91.5 & 93.4 & [PERSON] et al. [32] \\ \hline
**EfficientNet-B7** & PlantVillage & 94.3 & 95.2 & 96.8 & [PERSON] et al. [25] \\ \hline
**MobileNetV2** & Kaggle Dataset 1 & 91.2 & 92.1 & 94 & [PERSON] and [PERSON] [33] \\ \hline
**ResNet-50 + SVM** & Kaggle Dataset 1 & 93.9 & 94.5 & 96 & [PERSON] and [PERSON] [27] \\ \hline
**Vision Transformer (ViT)** & PlantVillage & 93.5 & 94.3 & 96.2 & [PERSON] et al. [28] \\ \hline  & PlantVillage & 98.5 & 98.7 & 98.6 & Proposed \\ \cline{2-6}
**BERT-ResNet-PSO** & Kaggle Dataset 1 & 98.3 & 98.5 & 98.4 & Proposed \\ \cline{2-6}  & Kaggle Dataset 2 & 98 & 98.2 & 98.1 & Proposed \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons between the proposed (BERT-ResNet-PSO) approach segmentation performance and other existing performances based on various datasets. Figure 8: Confusion matrix for class-wise Error. and the plant village dataset, using the identical methodology.