Plant disease detection and diagnosis are essential because they affect agriculture, food security, and economic stability. Manually identifying plant diseases is tedious and can cause errors in large cultivation areas. This requires a large amount of time and labor. The accurate manual detection of diseases requires a high level of ability and a lot of resources. The early detection of plant illnesses can reduce pathogen transmission and crop loss. Deep learning techniques and applications are required for plant disease detection to ensure accuracy, efficiency, and cost-effectiveness, as well as to reduce the need for manual labor and optimize the use of resources like pesticides and fertilizers. This review focuses on detecting plant diseases using deep learning techniques. In this work, we present the research progress on plant disease detection using deep learning technology. We expect that this work will be a valuable resource for researchers exploring and studying plant disease detection.