Integrating Genomics, Phenomics, and Artificial Intelligence for Next-Generation Crop Improvement

The growing global demand for food, combined with climate change, diminishing natural resources, and increasing incidence of biotic and abiotic stresses, necessitates innovative approaches for crop improvement. Traditional breeding methods have contributed significantly to agricultural productivity, but their limitations in addressing complex traits and rapidly changing environmental conditions have encouraged the adoption of advanced technologies. The convergence of genomics, phenomics, and artificial intelligence (AI) represents a transformative paradigm in modern agriculture, enabling precise characterization, prediction, and selection of superior genotypes. Genomics provides comprehensive insights into genetic architecture and functional variation, whereas phenomics facilitates high-throughput characterization of plant traits under diverse environments. Artificial intelligence and machine learning techniques offer powerful analytical tools for integrating multidimensional datasets and improving breeding efficiency. The combined application of these technologies accelerates the development of climate-resilient, high-yielding, and nutritionally superior crop varieties. This review highlights recent advances in genomics, phenomics, and AI-based approaches, their integration in next-generation breeding programs, major challenges, and future perspectives for sustainable agricultural development.