Crop diseases and pests have a significant impact on agricultural yield and quality. Digital image processing technology plays an important role in identifying crop diseases and pests. Deep learning has achieved significant breakthroughs in this field, with better results than traditional methods. The issue of crop pest and disease detection was defined. The deep learning method had stronger feature extraction ability, which can accurately capture subtle features, improve detection accuracy and reliability. Deep learning provided strong support for agriculture. The research of crop pest detection based on deep learning was summarized from three aspects: classful network, detection network and segmentation network, the advantages and disadvantages of each method were summarized, and the performance of existing research was compared. On this basis, the challenges that deep learning based crop disease and pest detection algorithms may face in practical applications were further explored, and corresponding solutions and research ideas were proposed. These findings and reflections had important guiding significance for promoting the development of crop pest detection technology in practical applications. Finally, the future trends of crop disease and pest detection based on deep learning were analyzed and prospected.