Plant Diseases Recognition on Digital Images using Swin Transformer

Abstract

Plant diseases seriously affect the safety of food production and must be quickly recognized and detected. In recent years, the traditional convolutional neural network has been widely used to diagnose plant diseases. Swin Transformer was used to train and evaluate the PlantVillage dataset, which includes 54303 healthy and unhealthy leaf images that divided into 38 categories by species and disease. The trained model based on Swin transformer learning achieves an accuracy of 98.1% on training data set, 98.7% on testing data set, which proves the feasibility of this method.