Abstract:
Plants play an essential role to human survival, from being the primary source of oxygen emissions to being a vital supply
of dietary ingredients. It keeps the ecosystem's general equilibrium, particularly in the food chain. Diseases will cause plants to
deteriorate in quality. Many botanists and domain experts research various ways to prevent plants from getting infected and preserve
their quality using computer vision and image processing integration on leaf images. The quality of the image collection provides a
substantial value for the classification model in identifying leaf diseases. Nevertheless, the amount of leaf disease image dataset is
very scarce. Since the performance of the models is determined on the overall quality of the dataset, this could compromise the
predictive models. Besides, existing leaf disease detection programs do not provide an optimized user’s experience. As a result,
although customers may receive an excellent interactive features programme, the backend algorithm is not optimized. This problem
may discourage users from applying the program to solve plant disease problems. In this paper, contrast boosting, sharpening, and
image segmentation are used to create an unprocessed leaf disease image dataset. Through the use of a hybrid deep learning model
that combines vision transformer and convolutional neural networks for classification, the algorithm can be optimized. The model
performance is evaluated and compared with the other methods to ensure quality and usage compatibility in the plantation domain.
The model training and validation performance is represented on graphs for better visualization .
