INTEGRATING GENOMIC SELECTION, GENE EDITING AND AI FOR NEXT-GENERATION INSECT-RESISTANT CROP BREEDING: A REVIEW

Abstract

Insect pests continue to be a significant factor limiting agricultural production, its profitability and world food security, and these are becoming more difficult to manage with climate change, increasing international trade and resistance to pesticides. Conventional resistance breeding has produced valuable cultivars, but its success is frequently limited by long breeding cycles, challenging phenotyping, polygenic inheritance, linkage drag and rapid evolution in insect populations. The integration of genomic selection, gene editing, and artificial intelligence offers a comprehensive approach to produce insect-resistant crops faster, with higher precision, and ensuring durability. Genomic selection can estimate the genetic value for untested individuals based on genome wide markers, gene editing can target resistance or susceptibility genes directly, and artificial intelligence can identify relevant information within genomic, phenomic, environmental, and entomological data. Combined, these techniques may enhance parent selection, cross design, editing-target identification, resistance phenotyping, and prediction by environment. Nevertheless, the effective implementation depends on having representative training populations, on efficient transformation systems, on robust field validation, on responsible data governance and on biosafety assessment and on accurate resistance stewardship. Here we review the basic concepts, current applications, constraints and future opportunities of the combined technology in the breeding of next-generation insect-resistant crops.