Abstract
Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI's role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding.