# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 3 The Proposed Hybrid Deep Learning Approach
### Proposed Approach
This framework produces as final output the performance metrics in the form of accuracy, precision, recall and F1-Score, which gives the whole picture of how well the classification model does. The two-stage approach employed in the BERT-ResNet-PSO architecture--where a BERT-based encoder performs image segmentation followed by classification through ResNet using the segmented mask--offers several distinct advantages over end-to-end models that classify diseases directly from whole images or patches. Primarily, this method introduces a level of spatial localization and region-specific focus that helps the model concentrate on disease-affected areas rather than being influenced by irrelevant background elements such as soil, lighting variations, or healthy leaf regions. By isolating the diseased portions through segmentation, the model ensures that the features extracted by ResNet are highly relevant to the classification task, thus improving accuracy and robustness. Figure 1: (**a**) Normal Leaf; (**b**) Red Spot; (**c**) White Spot; and (**d**) Leaf Crumple [5]. Figure 2: A multi-step machine learning framework for classifying cotton leaf images. Moreover, this segmentation-first strategy enhances model interpretability, as it provides visual masks indicating which regions influenced the classification decision--an essential feature for practical agricultural applications where transparency is crucial. It also allows for modular improvement, where either the segmentation or classification component can be upgraded independently. In contrast, end-to-end models may suffer from overfitting dataset-specific backgrounds and lack the interpretability and precision in learning from localized symptoms, which are particularly important when disease features are subtle or visually similar across classes. Therefore, this hybrid method provides both performance and practical deployment benefits in complex, real-world agricultural environments. A series of steps for cotton leaf disease classification and segmentation is shown in Figure 3 and described below:  
1. Pre-processing: The first step is pre-processing the cotton leaf images. This involves normalizing and resizing the images to ensure uniformity and compatibility with the model's input requirements. 2. Patch generation: The images are then segmented into patches. This process helps in dividing the image into smaller parts, which are then passed to the next layer for processing. 3. Patch embedding: The patches generated are embedded using a linear layer combined with positional embedding. This helps in structuring the data for further processing. 4. Transformer encoder: The embedded patches are fed into a transformer encoder, which captures spatial dependencies and provides a detailed representation of the input data. 5. Segmentation head: The output of the transformer encoder is processed by the segmentation head, which generates segmentation masks for the input image. 6. Loss function: The segmentation loss is calculated using a combination of Dice Loss and Cross Entropy Loss. These loss functions are used to measure the accuracy of the segmentation process and guide the optimization of the model. 7. Fitness function: In the optimization process, a fitness function evaluates the model's performance based on error minimization. It helps in guiding the model's learning by updating its weights and biases. 8. Ensemble learning: The approach incorporates ensemble learning by combining the outputs of multiple models. Each model contributes to the final prediction using a weighted voting mechanism. 9. Final output: The final output is determined by evaluating the classification performance across multiple metrics, including accuracy, precision, recall, and F1-Score. These metrics provide a comprehensive assessment of how well the model performs in classifying and segmenting leaf diseases.