Early and accurate crop disease detection disease is important for effective crop management. Deep leaning contains powerful techniques for automatically detection of crop disease through crop images. This review examines various deep learning models build in crop disease detection considering several key components such as transfer learning, GAN, attention mechanism, image segmentation, hybrid models, and lightweight models. In crop disease detection modeling, we explore various techniques in each step such as image processing, segmentation, feature extraction, model architecture. Modification in pre-trained model can enhances disease detection accuracy, where attention mechanism and hybrid models strengthen model ability to focus on critical features and performance improvement. A comparative analysis for combination of different techniques with deep learning model reveals model improvement which shows the need of integrating these techniques to achieve model’s optimal performance and also identifies a gap in research concerning disease detection in plants. Future research should aim to develop unified model capable to detect disease across all parts of plant and disease progression stage to automate disease diagnosis process.