The field of crop disease detection and generation is witnessing a significant shift towards unified multimodal models that can seamlessly integrate text and image data. These models have shown remarkable performance in generating high-quality synthetic images and detecting pests with high accuracy. The focus is on developing architectures that can effectively fuse visual and textual features, enabling robust and interpretable results. Notable advancements include the development of multi-scale cross-modal fusion networks and unified visual generators that can perform both text-to-image generation and instruction-based image editing tasks. # Noteworthy papers include PhytoSynth, which leverages multi-modal generative models for crop disease data generation, and MSFNet-CPD, which introduces a multi-scale cross-modal fusion network for robust pest detection. # Additionally, Mogao and Ming-Lite-Uni have made significant contributions to the development of omni-modal foundation models and unified architectures for natural multimodal interaction.