AI Models for Advancing Plant Genetics and Genetic Engineering
Abstract
Artificial intelligence (AI) has emerged as a transformative force in plant genetics and genetic engineering, addressing long-standing challenges in trait complexity, breeding cycle duration, and genome-editing precision. The convergence of machine learning, deep learning, and multi-omics integration enables the decoding of complex gene–gene and gene–environment interactions, revealing insights that surpass conventional statistical methods. Recent applications include AI-assisted prediction of quantitative trait loci, hybrid performance, and gene regulatory networks, as well as advanced modeling for CRISPR guide RNA design, off-target prediction, and DNA repair pathway outcomes. Moreover, AI is accelerating metabolic engineering by simulating flux distributions and employing generative models to design novel biochemical pathways, while breakthroughs such as AlphaFold2 have revolutionized protein and enzyme engineering. Case studies across rice, wheat, maize, soybean, and tomato demonstrate AI’s contributions to drought tolerance, disease resistance, yield enhancement, and quality trait prediction, with industrial applications extending to biofuel optimization and biodegradable material development. Despite these advances, challenges remain, including data scarcity, model interpretability, infrastructural limitations, and ethical considerations. The future promises even greater integration through explainable AI, robotics-enabled closed-loop breeding, digital twins, and quantum computing, which collectively hold the potential to redefine crop improvement. By harnessing AI responsibly and inclusively, plant biotechnology can accelerate progress toward global food and nutritional security, climate resilience, and sustainable agriculture.
