Accurate identification of papaya leaf and fruit combine infection using vision transformers

Early disease management of plant infections depends heavily on precise identification methods for papaya leaf and fruit combine infections because this determines the effectiveness of disease control measures. Vision Transformers (ViTs) serve as the basis for this research which aims to analyze diseases in papaya leaves and fruits. The model benefits from ViT technology because it surpasses other image analysis methods at detecting complex spatial structures within pictures to differentiate plant health status. The proposed method raises detection precision standards and boosts robustness which creates a promising instrument for agricultural monitoring as well as management. Experimental findings show that Vision Transformers deliver better results than conventional convolutional neural networks thus indicating their potential to enhance automated disease identification in plants for sustainable farming practices.