<p>The improvement of agricultural practices relies on various elements, including plant identification, disease detection, and soil analysis. Plant diseases significantly affect agriculture, resulting in decreased production and quality of crops. The application of computer vision technology for diagnosing crop diseases plays a crucial role in modernizing agriculture and boosting field production efficiency. This technology is characterized by its noninvasive approach, rapidity, real-time capabilities, and accuracy. Deep learning (DL), a recent advancement in computer vision, has become a central focus in agricultural plant protection, offering the potential to reduce the bias associated with the manual selection of disease spot features. This article examines the methods and materials employed in the field of automated plant disease detection, including various DL models, modified CNNs, vision transformers, and the current trends in DL-based disease detection. It also emphasizes preprocessing techniques, such as image size, color annotation, feature extraction, and segmentation, as well as the evaluation of different models based on their classification and detection capabilities. Furthermore, this study bridges the gap between academic research and practical agricultural applications by providing guidance on the applicability of these models in production environments. This comprehensive review offers valuable insights into the present state and future directions of deep learning in plant disease detection, serving as an essential resource for researchers, academics, and practitioners in the field of precision agriculture. Our work aligns with SDG2.</p>