Artificial Intelligence Approaches in Plant Digital Multiple Omics

The artificial intelligence (AI) integration in plant digital multiple omics has transformed plant research by increasing data assessment, explanation, and predictive modeling. AI-driven applications, encompassing deep learning (DL) and machine learning (ML), enable the extraction of impactful insights from large-scale genomic, proteomic, phenomic, metabolomic, and transcriptomic datasets. These technologies facilitate correct trait choice, gene expression analysis, and stress tolerance prediction, thereby increasing crop advancement programs. AI models, such as recurrent neural networks and convolutional neural networks (CNNs), have been successfully used for accurate breeding and high-throughput phenotyping. Furthermore, incorporative AI frameworks permit a systems biology strategy and bridge gaps among omics layers to elucidate complicated plant-climate interactions. Current innovations in AI-assisted image analysis and natural language processing further increase digital farming by yield forecasting and automating disease detection. Despite its revolutionized potential, AI applications in plant multiple omics pose problems such as limited annotated datasets, data heterogeneity, and the requirement for standardized computational frameworks. Determining these constraints needs interdisciplinary cooperation between agronomists, data scientists, and biologists. Future AI-driven multi-omics applications promise to develop climate-resilient crops, improve agricultural sustainability, and increase food security through automated decision-making and predictive analytics. Future studies should focus on producing AI-powered, multi-omics platforms based on clouds with organized data-sharing protocols to increase reproducibility and interoperability in plant sciences.