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Cotton is a vital cash crop, contributing significantly to the global textile industry and the livelihoods of millions of farmers worldwide. However, diseases such
as bacterial blight, leaf curl virus, and whitefly infestations pose a severe threat
to cotton production and quality. Timely detection and accurate identification of
these diseases are crucial for implementing effective control measures and ensuring
crop health by exploring multiple state-of-the-art deep learning models, including
CNNs and transformers. The research utilizes a diverse dataset of cotton plant
images, encompassing healthy and diseased leaves, to train and fine-tune the deep
learning models and Vision transformers. Additionally, we will focus on evaluating the models’ capability to detect varying intensities of whitefly infestations,
which is critical for assessing disease severity and implementing appropriate control strategies. The models were cross-validated and regularized to improve the
models working. This study has the potential to contribute significantly to the
field of computer vision, particularly for cotton disease detection.