# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 2 Related Work  
Several studies have been conducted on the development of deep-learning approaches for improving cotton leaf disease detection. [PERSON] et al. [11], for example, developed a deep learning approach for predicting cotton leaf diseases with fine-tuning and transfer learning. A smart web application was made by the study with the Xception model to yield 98.70% accuracy. Aided by transfer learning, the approach can adapt pre-trained models to disease detection tasks with relatively little need for extensive labeled datasets and the model training time is lowered. The approach heavily relies on pre-trained models, which may not fully capture the unique features of different environmental conditions and disease manifestations, as this application is used to assess for disease on real-life plants, minimizing cotton yield losses and reducing workload. However, the accuracy may also drop when it is applied to various datasets that differ from the original training data. [PERSON] et al. [11] proposed a deep learning-based approach for detecting cotton leaf diseases by transfer model fine-tuning using the tuning of layers and variables of different available models. The study looks at the effectiveness of different fine-tuning transfer-learning models, such as VGG-19, VGG-16, Xception and Inception-V3, for the prediction of cotton diseases on a publicly available cotton database. The approach involves data collection, pre-processing, model development, training, evaluation, and mobile application development. The images undergo pre-processing through various tools such as VGG-19, InceptionV3, Xception, and VGG-16. A selection of 20% of the images is utilized for training models, with metrics employed to evaluate cotton eases. Ultimately, the most effective model is implemented within a web application. The Xception model, with a 98.70% accuracy, was chosen for a web-based smart application to help agricultural practitioners predict cotton diseases in real time, thereby enhancing production capacity. The model accurately identifies cotton leaf diseases, providing a novel approach for the automated diagnosis of more plant species. The DL-based cotton disease detection model has limitations, including class imbalance, skewed predictions, adaptability to novel disease cases, and inability to deliver essential features, requiring the development of models for feature extraction and selection. [PERSON] et al. [12] suggest Convolutional Neural Networks (CNNs) for disease detection, enabling farmers to take timely actions and reduce crop losses. CNNs recognize disease-specific patterns in plant leaves. The study employed two distinct methodologies for the detection and categorization of leaf diseases, employing both deep learning and traditional machine learning techniques. The approach involves several steps in the detection and labeling of leaf diseases, together with data selection, pre-processing, and data transformation. The study divides the dataset into four categories: Alternaria leaf, grey mildew, leaf reddening, and healthy leaf. The research pre-processes the data and transforms it into NumPy arrays to standardize RGB values. A data augmentation strategy was used to address irregularities. The study further applies batch normalization and dropout layers to the CNN model. Performance metrics guide the selection of the optimal model. The study suggests that machine learning in agriculture can automate disease identification, increasing crop yields and efficiency. Precision agriculture uses CNNs to detect cotton leaf diseases, a significant step. However, traditional models are labor-intensive and time-consuming, making crop assessment challenging and limiting access to professionals in certain regions. [PERSON] et al. [13] explore a diverse range of image processing concepts, like image acquisition, feature extraction, image pre-processing, database formation, and artificial neural network classification.