Abstract : Cotton is one of the most important cash crops in the world, and many plant diseases have a big effect on how much it grows. To protect food and economic security, it is important to find and treat cotton plant diseases as soon as possible. Machine learning (ML) and deep learning (DL) methods have been widely used in the last few years to automatically find plant diseases using pictures of leaves. Nonetheless, current studies present findings derived from varying experimental conditions, datasets, and evaluation metrics, complicating direct comparisons. The agricultural productivity and the textile industry are at a high risk due to cotton plant diseases. These diseases should be detected early and when it is necessary as this will make the loss minimal and may lead to better yield in crops. This paper will involve a detailed comparative performance evaluation of deep learning and conventional machine learning algorithms in the detection of cotton plant diseases. Deep learning networks, such as ResNet18 and Swin Transformer, are tested in comparison with the traditional machine learning algorithms, such as Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes. The experiment is done on a publicly available dataset of a cotton disease. Findings indicate that Transformer-based models are much better than CNN and traditional methods because they can capture global contextual relationships. The Swin Transformer has the most accurate and ROC-AUC and is thus suitable when faced with complex plant disease classification problems.