# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 2 Related Work
The proposed technique can detect cotton leaf illnesses early, reducing environmental and human health risks. It can distinguish disorders with minimal computational effort, allowing for pest management models. The model has a 98.9% accuracy rate in identifying conditions in cotton leaves, making it useful for farmers in early disease identification. Environmental variables, including diseases induced by bacteria, fungi, and other pathogens, can profoundly affect productivity. [PERSON] [17] developed a new deep-learning model for disease identification in cotton. The Grey Wolf Optimization (GWO) algorithm refines the advanced DL network. The GWO algorithm is introduced for the first time in this study. The image is used by this model to ascertain whether the cotton is diseased or healthy. It constitutes a deep CNN model. The study precisely attends to the model's development to ensure its specificity to the problem at hand. The grey wolf optimization Algorithm has been used in this research to prove the optimality of the model design. This process will determine the most effective design. The study has conducted a proportional analysis between the proposed model and the commonly referenced VGG19, ResNet50, and InceptionV3 models in the literature. The results acquired show that the accuracy metric for the proposed model equals 1.0. The accuracy scores for the other models were 0.726, 0.934 and 0.943, respectively. The study shows how hard optimization problems are in the real world and how much we need better solutions because of the problems caused by nonlinear features, lots of decision factors, and the complicated limits that come with traditional models. [PERSON] et al. [18] propose a model for developing a machine learning-based system for classifying diseases in cotton plants using leaf images, which involves data acquisition, pre-processing, model training, ensemble model development, model evaluation, and result analysis. The study investigated the predictive capabilities of three distinct models: Support Vector Machine (SVM), MultiClass SVM, and an Ensemble Model that combines Random Forest and Decision Tree approaches. The specific task was binary classification, where we aimed to categorize inputs as either \"Diseased\" or \"Healthy.\" The study evaluated these models not only for their class predictions but also for their associated class probability estimates, which provide insights into the models' confidence in their predictions. All three models unanimously forecasted the input as \"Diseased.\" The SVM and MultiClass SVM models showed moderate confidence in their 'Diseased' predictions, with a probability estimate of 76.77%. However, the key difference lies in the confidence levels conveyed through their class probability estimates, which are crucial for understanding their reliability. [PERSON] et al. [19] examine the several forms of cotton leaf diseases and their prevalence, the relationship between environmental factors and these diseases, and suggest an automated detection approach for Cotton Leaf Cucumber Disease (CLCuD) using cotton leaf pictures. The research establishes a self-compiled dataset for the classification of CLCuD, utilizing visual symptoms derived from various images. Pre-processing steps are thoughtfully implemented to extract features, and a refined DL model is presented for the purpose of predicting susceptibility levels. The research uses Convolutional Neural Networks (CNN) to assess these models based on two separate databases: one sourced from the publicly accessible Kaggle database and another derived from a proprietary source. Agricultural specialists provided their insights to annotate the dataset, drawing upon their knowledge in recognizing unusual growth forms and features. Data augmentation significantly improves the accuracy of model performance, utilizing deep features that facilitate both testing and training processes. Compared to other models, the CNN model outperforms, achieving an accuracy of 99% with the proprietary database.