Wheat is one of the crucial cereal crops globally, and its productivity is affected by various diseases. Therefore, the timely and precise detection and classification of these diseases are important. The advancements in deep learning (DL), especially convolutional neural networks, have observed significant evolution in wheat disease identification. This article provides a comprehensive overview of research works, which effectively utilized DL for wheat disease prediction and classification. The paper begins by introducing the importance of wheat disease management and the challenges associated with traditional disease identification methods. The survey further explores the general architecture of the DL-based wheat disease prediction framework the study highlights the significance of DL models in achieving accurate and effective disease classification. In addition, it demonstrates the challenges associated with different DL models relative to wheat disease identification. Furthermore, it examines the research works related to wheat disease detection using DL models and highlights the limitations of those methods. Finally, the survey provides the future research scope for an improved disease identification model.
