Integrating NLP with Climate and Genomic Data for Climate-Resilient Crop Breeding

Modern crop breeding is being revolutionized by Artificial Intelligence (AI), especially Natural Language Processing (NLP), which makes it possible to analyze unstructured data like reports, patents, and scientific literature efficiently.Big data issues impede advances in understanding the intricate biological processes determining agricultural attributes, despite advancements in phenomics, enviromics, and other "omics" methods.By automating literature mining, detecting gene-trait connections, and combining knowledge from multi-omics datasets, NLP tackles these issues.By improving accuracy and facilitating data integration, it improves high-throughput phenotyping, genotyping, and enviromics.Researchers can enhance breeding strategies for climate-resilient crops and speed up gene identification by fusing NLP with "omics" techniques.