Wheat leaf disease classification using vision transformer based deep learning approach

Abstract

Wheat crops can be affected by numerous fungal diseases like leaf rust, stripe rust, and stem rust. These diseases can significantly reduce yield and grain quality and also result in highly economic damage. Detection of diseases at an early stage is the main and most complex task for farmers due to common morphological properties like color, shape, texture, and edges. Deep learning (DL) offers a powerful solution for detecting wheat crop diseases due to its ability to accurately identify diseases, even in complex field conditions, and facilitate early intervention. By analyzing images, DL models can learn complex patterns associated with various diseases, enabling faster and more reliable diagnoses than traditional methods. In this paper use DL models followed by a vision transformer model (ViTM) to extract multilevel features to focus more on the disease-infected area on the plant with enhanced accuracy. DL models extract hierarchical features, whereas transformer models are utilized to capture global dependencies and contextual relationships within the image. In the proposed research evaluated various DL models, InceptionV3, EfficientNet-B0, visual geometry group 16 (VGG-16), and ResNet-50 with visual transformers on the wheat disease detection dataset of 3679 images and achieved accuracy rates of 98.7%, 97.6%, 93.9%, and 97.5%, respectively. The experiment proved that InceptionV3 outperforms the other models.