Diagnosis And Accurate Classification of Apple Leaf Diseases Using Vision Transformers

Abstract

In agriculture, plant pests and diseases are one of the major factors that reduce fruit and vegetable yields. Early detection of foliar diseases, which cause significant yield losses in apple production, is of great importance for the sustainability of agricultural production. The subjective and inefficient nature of traditional manual and tool-based diagnostic methods is insufficient to provide solutions that meet scientific and production standards. In this context, pattern recognition and deep learning techniques offer more effective alternatives for automatic image analysis and classification. In this study, Vision Transformers are used for disease classification from apple leaf images. The dataset consists of images of healthy apple leaves and apple leaves with different diseases. The results show that Vision Transformers is effective in classifying apple leaf diseases with high accuracy. This study makes an important contribution to increasing the use of digital technologies in agriculture and plant health monitoring.