# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 3 The Proposed Hybrid Deep Learning Approach
### BERT-Based Encoder for Image Segmentation Dataset  
The Plant Village Extension Cotton Leaf Disease dataset is the extension of the well-known PlantVillage dataset, specifically developed for cotton leaf disease identification and classification. This dataset consists of over 4000 high-resolution images of cotton leaves with numerous disease conditions, including avia rium blight, leaf curl virus, fusarium wilt, Alternaria leaf spot, and healthy leaves. These images are in standard formats (JPEG, PNG, etc.) and are accompanied by a CSV annotation file that tells what disease each image has since the structure is easy to integrate into machine learning pipelines for a typical supervised learning task. This dataset's ability to precisely predict disease, its classification detail and image quality make it suitable for image classification and disease detection applications for researchers and practitioners to work on model development and evaluation. Normally, the Cotton Leaf Disease Dataset (PlantVillage Extension) is available at repositories such as Kaggle, GitHub, and the actual PlantVillage dataset website. The dataset can be obtained from the following link: [[https://www.kaggle.com/datasets/emmarex/plantdisease](https://www.kaggle.com/datasets/emmarex/plantdisease)]([https://www.kaggle.com/datasets/emmarex/plantdisease](https://www.kaggle.com/datasets/emmarex/plantdisease)) accessed on 26 August 2024.  
Figure 2 shows that the BERT (Bidirectional Encoder Representations from Transformers) model is applied to segment cotton leaf images. With this adaptation, BERT can learn both spatial and contextual relationships within the image, which is necessary to differen  
Figure 3: Proposed Approach Implementation Flow.  
tiate diseased from normal 'healthy' areas of the leaf. To that end, they propose using a Vision Transformer (ViT) that uses the image in the same manner as the text, breaking the image into patches. Then, we process these patches with the transformer-based architecture inspired by BERT. The image patch generation can be expressed as:  
\[P_{i}=\text{Patch}\big{(}I_{\text{seg}},i,p\big{)} \tag{1}\]  
where \(P_{i}\) denotes the \(i^{th}\) patch of the segmented image \(I_{\text{seg}}\), and \(p\) indicates the patch size. These patches are subsequently embedded using a linear transformation. The embedding of each patch is represented as:  
\[E_{i}=\text{Linear}(P_{i})+\text{PositionEmbedding}(i) \tag{2}\]  
Here, \(E_{i}\) is the embedded patch and \(\text{PositionEmbedding}(i)\) captures the spatial position of patch \(i\). This step is essential because BERT-like models do not inherently understand spatial information as conventional neural networks do. The embedded patches are then fed into several transformer encoder layers to allow the model to attend to different parts of the image, capturing complex relationships within the image data. The output of this transformer encoder is defined as:  
\[Z_{i}=\text{TransformerEncoder}(E_{i}) \tag{3}\]  
where \(Z_{i}\) is the output embedding for the \(i^{th}\) patch after passing through the encoder layers.