Plant leaf disease control is crucial given the prevalence of plant leaf diseases around the world. The most crucial aspect of controlling plant leaf diseases is appropriately identifying them. Deep learning-based plant leaf disease recognition is a viable alternative to artificial methods that are useless and inaccurate. The proposed work aims to combine plant leaf disease datasets from various countries, review current research and progress in deep learning algorithms for plant disease recognition, and explain how different types of data are developed and used in this area using different deep learning networks. The feasibility of several network models for deep learning-based plant leaf disease detection is discussed. Solving shortcomings such as sunlight irradiation in plant planting conditions, similar disease incidence of different plant leaf diseases, and varied symptoms of the same disease in different damage periods or infection degrees are all essential study topics in the growth of this discipline. To address the concerns raised above and establish the field's future development potential, we must research high-performance neural networks based on the benefits and downsides of diverse networks. The proposed work can serve as a foundation for future research and breakthroughs in the identification of plant leaf diseases.