Demand for global food productivity increased from past many years. At the same time, agriculture is facing number of challenges from pests and diseases. Thus, it is necessary to predict and detect various diseases in agriculture. The efficiency and accuracy of predicting and detecting agricultural diseases will play a vital role in improving productivity. Technology play a major role in predicting and detecting agricultural diseases. Deep learning (DL) technology is one of the potential technology, that can be used in the field of agriculture to predict and detect various diseases. This paper focuses on reviewing the use of various DL techniques to predict and detect the crop leaf diseases. Further, discusses the on current trends, challenges, and future research directions in using deep learning for crop leaf disease identification and broader plant disease and pest management strategies.