Predictive Artificial Intelligence for Climate-Resilient Crop Breeding: Integrating Genomics, Phenomics, and Climate Modeling
DOI:
https://doi.org/10.32350/sir.101.02Keywords:
climate resilient crops, crop breeding, genome prediction, machine learning, phenomics, predictive artificial intelligenceAbstract
Global food security is increasingly threatened by climate change, which intensifies both biotic and abiotic stresses on crops, leading to reduced yield stability and productivity. Conventional breeding approaches are insufficient to address these challenges due to long breeding cycles, strong genotype–environment interactions, and limited capacity to predict crop performance under future climate scenarios. Predictive artificial intelligence (AI) offers a powerful solution by integrating genomics, phenomics, environmental, and climate data to model complex traits, stress tolerance, and genotype performance across diverse agroecological conditions. This review synthesized recent advances in Machine Learning (ML), Deep Learning (DL), genomic prediction, high-throughput phenotyping, and climate modeling that collectively support the development of climate-resilient crop varieties. The application of predictive AI in plant breeding enhances selection accuracy, accelerates breeding decisions, reduces dependency on costly and time-consuming field trials, as well as improves resource-use efficiency. By enabling data-driven decision-making, AI-based approaches significantly improve the precision and scalability of modern crop improvement programs. Looking ahead, the integration of predictive AI with explainable modeling frameworks, multi-omics datasets, and genome-editing technologies holds significant promise for accelerating the development of high-yielding, resilient, and sustainable crops, thereby contributing to long-term food security under changing climatic conditions.
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