Decoding Crop Stress Responses through Multi-Omics Integration and Computational Modeling for Climate-Smart Farming

The rapidly increasing effects of climate change are a serious threat to agricultural productivity and food security worldwide. To address this, the paradigm of crop stress biology is shifting due to the incorporation of multi-omics technologies and computational intelligence. This review highlights how holistic omics approaches, enhanced by artificial intelligence (AI) and machine learning (ML), are deciphering the intricate molecular networks governing plant stress responses. Such technologies are transforming the traditional breeding approach to crops into a predictive science, providing high-resolution insights into gene functions, stress-responsive pathways, and phenotypic traits. We discuss AI applications in dynamic crop monitoring, gene prioritisation, trait selection, and CRISPR-based genome editing, and highlight how they can be used to improve crop resilience and adaptability. In addition, climate-smart farming solutions are being developed in the wake of the emergence of digital agriculture, a product of big data and systems biology technologies. All these integrative tools, combined, can create opportunities for sustainable agriculture that have never existed before and ensure food production in a time of climatic uncertainty.