32 / SEEDWORLD.COM SEPTEMBER 2026 breeders’ attention because it may be possible to further improve successful hybrids without this trait by introducing it. “Towards that end we have identified about one and a half dozen genes that contribute to the re-orientation trait,” he says. “Markers for these genes could be used by breeders to add this trait to future hybrids. Alternatively, genome editing technology could be used to introduce this trait into otherwise promising inbred parents of future hybrids.” For breeders, the value lies in what comes next, says ISU Department of Mechanical Engineering professor Baskar Ganapathysubramanian. His team's framework combines realistic 3D recon structions with simulations to evaluate how canopy architecture influences light interception before large-scale field trials begin. “The value for breeders,” he explains, “is that this approach can help test architectural traits, such as leaf orientation, row spacing, plant spac ing, leaf angle, curvature and vertical leaf arrange ment in a realistic canopy, before doing large field experiments.” The team published further work using a com putational model and optimization framework to search for corn canopy architectures that maximize light interception. “This gives breeders more insight into which combinations of architectural traits may be prom ising, rather than looking at one trait at a time,” Ganapathysubramanian says. He explains further that using AI to generate promising ideotypes — ideal plant architectures for breeders to target “emphasizes that AI is becom ing a major force multiplier” for breeders. “The concept of an ideotype, a target plant architecture engineered for optimal performance, goes back to C.M. Donald's work in the late 1960s, but it has historically been very difficult to opera tionalize,” Ganapathysubramanian notes. “Breeders can only field-test a handful of architectural traits at a time, and the combinatorial design space (leaf angle, azimuth, curvature, vertical spacing, plant and row spacing and other factors) is enormous.” He says AI changes this picture in two ways. First, Ganapathysubramanian says, “virtual fields and canopy simulations let us evaluate thou sands of architectural combinations in silico, long before any seed goes in the ground. Second, opti mization algorithms can search this design space efficiently to identify promising ideotypes, rather than evaluating one trait at a time.” Coupled with recent advances in breed ing (genomic selection, gene editing and high- throughput field phenotyping), he says this creates a tight loop – AI proposes candidate ideotypes, breeders evaluate them and work to realize them genetically, and the resulting plants generate new data that refines the next round of models. AI Is Changing Corn Breeding in Four Ways Phenotyping AI extracts traits from millions of field images. Virtual Fields Digital crop models test thousands of plant architectures before field trials. Trait Discovery Machine learning helps identify genes linked to desirable characteristics. Breeding Decisions AI helps breeders prioritize which combinations to advance, reducing time and cost. Researchers reconstruct individual corn plants from 3D point clouds, creating digital plant models that can be analyzed for leaf orientation, canopy architecture and light interception. PHOTO: NASLA SALEEM, IOWA STATE UNIVERSITY
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