Attention Based Mapping for Plants Leaf to Classify Diseases using Vision Transformer

Agriculture plays a crucial role in meeting the basic food needs of a growing global population. The growth of grains and vegetables is essential not only for nutrition but also for the world economy. One of the major challenges associated with cultivating plants is the prevalence of diseases on the leaves, which can significantly impact the plant's growth and development. Traditional techniques for diagnosing these illnesses can be expensive and time-consuming. To solve this problem, researchers are investigating the potential of intelligent systems that depend on the advanced deep-learning architecture. In this study, researchers have replaced CNN with a new method called Vision Transformer for classifying plant leaf diseases. There are three vision transformer models are compared in this which are ViT1, ViT2, and pre-trained ViT_b16 and the test accuracy achieved by them are 85.87%, 89.16%, and 94.16%. The results show that our proposed model is able to identify the specific region of the leaf where the disease is present, providing valuable insights for farmers.