Crop diseases pose a grave threat to global agricultural productivity, food security, and economic stability, although they are exposed to both abiotic stressors like drought, salinity, and temperature fluctuations and biotic factors encompassing pests and pathogens. Conventional detection methods cannot adequately reflect the complexity of the interactions involved in them and thus call for more advanced approaches like deep learning. This study is aimed at critically reviewing the application of deep learning models for crop disease detection, focusing on covering both biotic and abiotic factors for an accurate and timely indication of its development. Several deep learning architectures, including CNNs, RNNs, and FCNs, have been applied with great potential for disease identification in either image processing or temporal analysis. The review also identifies key challenges in the deployment of such models, which include issues concerning data quality, high computational demand, and the requirement for more comprehensive datasets. The improved future directions are to make optimization of models, automated techniques, and fusion of data with a view to further improving deep learning models in its application areas of real agricultural environments.