Artificial Intelligence for Plant 3D Spatial Omics

Recently, omics and artificial intelligence (AI) have been emerging as important tools for biological studies, including plants. It begins with outlining those four core disciplines, including genomics, transcriptomics, proteomics, and metabolomics, which combine to strengthen plant systems-level research through multi-level analyses. The 3D omics concept surpasses classical omics by gaining spatial dimensions to map cellular components inside plant tissues, hence giving much larger perspectives on developmental processes and stress responses. Processing of large datasets that flow out of these technologies highly improves AI 3D omics. These algorithms can be easily integrated with data, predictive modeling, and advanced phenotyping of crops through machine learning and deep learning. Subsequently, this results in the discovery of complex molecular interactions that develop crops with resilient varieties. Besides this, several applications of AI in developing disease resistance markers and crop development through targeted breeding strategies have been narrated in this chapter. It presents challenges such as a lack of clarity in the transparency of the algorithm and requires heavy intensive computation. Such would need interdisciplinary collaborations among plant biologists, data scientists, and AI experts to fully unleash the total potential of AI-assisted 3D omics, which aims to attempt to solve some of the global challenges about climate change and food security issues. All this synergy is promised to revolutionize crop improvement strategies and promises much deeper insights into plant biology in increasingly complex environments.