AI is being increasingly integrated into crop breeding to predict crosses, identify gene networks, and tailor genetics to specific environments, thereby speeding up decision-making and trait improvement.

Avalo offers AI-driven predictions of complex traits and environment-genetics matching to accelerate breeding, emphasizing that AI informs decisions rather than directly editing seeds.

Plantik Biosciences focuses on regulatory (non-coding) DNA to boost stress responses, leveraging multi-omics data and CRISPR edits, with a licensing and services model highlighted by founders at the Paris-Saclay Summit.

Chinese researchers are advancing concepts like Click Breeding and Breeding 5.0 that combine genomic prediction, crop modeling, and automation to design and test multigenerational breeding programs.

Rainbow Crops maps gene networks with systems biology and AI to prioritize genetic modifications and validate trait performance, backed by a Gates Foundation project and recent funding of $11.25 million in mid-2026.

Crop Science Centre at the University of Cambridge blends AI, computer vision, multi-omics, and phenotyping to advance crop improvement, collaborating with Bayer, RAGT, and Syngenta.

Canadian researchers discuss streamlining AI-powered systems for monitoring, quality control, and CRISPR-outcome prediction in breeding workflows, noting challenges and resource-saving benefits.

Velsera automates marker scoring in genotyping, reducing processing time and enabling high-throughput, customized trait-discovery workflows.

University of Queensland researchers are testing ensemble genomic prediction models to improve accuracy for complex traits, with evidence that ensemble methods may outperform single-model approaches.

AI-CropBreed, a Europe-based platform, provides AI-powered services for identifying climate-resilient varieties and offers centralized data management across breeding programs.

Heritable Agriculture offers an AI platform that creates digital twins of crops to predict cross outcomes and performance, aiming for greenhouse strawberry breeding and market-ready varieties by 2028.

Verinomics provides an AI-driven population genomics platform and a transgene-free gene editing approach for vegetatively propagated crops, targeting faster trait discovery and breeding decisions.