Artificial Intelligence-Assisted Omics Techniques in Plant Defense: Recent Advancements and Future Prospects

Physiological and biochemical processes in plants are used to address different biotic and abiotic threatening factors. Multi-omics approaches including proteomics, transcriptomics, genomics, and metabolomics have transformed the understanding of these immune responses by providing detailed molecular clues on how organisms respond to stressors. Nevertheless, because of highly dimensional and complicated omics information, it is often challenging to decipher them systematically. This chapter focuses on the application of AI algorithms, primarily deep learning and machine learning, as effective techniques for analyzing high-dimensional omics information in plant immune responses. It offers a brief introduction to the essential omics methods, describes the general difficulties and the present restraints for AI integration into omics, and contemplates possible advances in this rising field. Finally, integrating an application of AI in omics could provide a strong weapon in understanding the molecular basis of plant defense mechanisms and hence comes with the potential of improving crop protection methods under changing climate conditions and newly emerging pathogens.