# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 5 Conclusions  
This study presents a hybrid deep learning approach based on BERT-ResN PSO for efficient cotton plant disease detection. The study combines image pre-processing, a BERT-like encoder, ResNet-based feature extraction and particle swarm optimization; this approach significantly improves detection accuracy. The dataset is made up of more than 4000 images of various diseased symptoms, say aviarum blight, leaf curl virus, fusarium wilt, alternaria leaf spot and healthy leaves. The experimental results, with an accuracy of 98.5%, demonstrate the effectiveness of this hybrid deep-learning approach in identifying disease regions. Compared to existing deep learning techniques such as ResNet50, VGG19, and InceptionV3, this hybrid deep learning approach offers superior precision and recall; the proposed model showed superior performance, with precision at 98.2% and recall at 98.7%, making it a promising solution for early disease detection. The performance of the proposed BERT-ResNet-PSO approach based on the dataset 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%. A comparison of classification performance between the proposed (BERT-ResNet-PSO) model and other existing approaches on different datasets' performance 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. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline PlantVillage & BERT-ResNet-PSO & 99.0 & 98.9 & 98.8 & 98.85 \\ \hline  & BERT-ResNet-AdamW & 98.5 & 98.3 & 98.2 & 98.25 \\ \hline  & BERT-ResNet-SGD & 98.3 & 98.1 & 97.9 & 98.0 \\ \hline Kaggle Dataset 1 & BERT-ResNet-PSO & 98.8 & 98.7 & 98.6 & 98.65 \\ \hline  & BERT-ResNet-AdamW & 98.3 & 98.1 & 98.0 & 98.05 \\ \hline  & BERT-ResNet-SGD & 98.0 & 97.8 & 97.6 & 97.7 \\ \hline Kaggle Dataset 2 & BERT-ResNet-PSO & 98.5 & 98.3 & 98.2 & 98.25 \\ \hline  & BERT-ResNet-AdamW & 98.0 & 97.8 & 97.6 & 97.7 \\ \hline  & BERT-ResNet-SGD & 97.6 & 97.5 & 97.2 & 97.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of state-of-the-art approach. 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 0.99. The results indicate that the deep learning BERT-ResNet-PSO approach can help farmers make appropriate responses by determining the severity of the disease at an early and accurate diagnosis. It includes the ability to apply not mass spraying of fertilizer or pesticides all over the whole farm, for example, but targeted treatments, such as the correct type of fertilizers and pesticides, to areas. This model is a means of cutting back on environmental impact while boosting crop production and warding off plants from diseases. Finally, this research offers an important tool for improved management of cotton plant diseases, and benefits can be derived from both conventional and organic farming practices. The application of deep learning models in agriculture can improve disease management, increase productivity and decrease the environmental footprint of cotton farming by enabling early detection and precise interventions. Despite its success, the study acknowledges limitations, such as the need for optimization of certain features and adaptability to varying environmental conditions. Future work could include iterative improvements for feature optimization and the expansion of the model to classify additional plant diseases. Methodology, C.S. ; Supervision, S.W. and S.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article.