Enhancing Accuracy in Predicting and Classifying Phytopathology Using Transformer Architecture

Plant agriculture remains the essential basic source that produces national revenue for numerous countries. Plant diseases which develop from bacterial and fungal and viral infections cost agribusiness entities globally substantial funds. Plant disease needs protection of both its quantity production and its quality preservation for proper disease monitoring. Plant disease detection needs to be identified properly. The symptoms from plant disease syndrome affect varied elements within the plant structure. The infectious agent primarily appears in different leaf portions. Multiple scientists employ a combination of plant disease recognition methods which include computer vision along with deep learning technology and few-shot learning techniques and soft computing strategies to analyze leaf images. These approaches enable farmers to immediately execute suitable measures which prevent worsening of both crop quality and quantity. Disease recognition success becomes possible by proper selection of features through advanced technological methods that minimize origin disadvantages while enhancing research velocity. The development of particular molecular methods aims to halt and minimize disease transmission. Therefore, this article aids the researcher in automatically identifying plant diseases and offers specific diagnosis methods to avoid sickness. Additionally, a few upcoming projects in illness classification are also covered.