A multi-scale feature extraction and fusion deep learning method for classification of wheat diseases

HIGHLIGHTS

• What: The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. The aim of this study is to identify the type of yellow rust disease infection in wheat by using computer models. The aim of this study is to perform a thorough analysis of the most recent studies on Wheat Disease (WD) prediction models that have been published and discussed. In this article, an automated system, the Yellow-RustXception deep convolutional neural_network-based model, was introduced.
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