32 SEEDWORLD.COM/CANADA JULY 2026 > With imaging platforms, aerial analysis and growing datasets, breeders are beginning to shorten selection timelines and improve accuracy. > By Treena Hein, Seed World Contributor > initializing_model(“plant_reader”) > training... > Teaching Teaching Machines to Machines to Read Plants Read Plants > status: learning IT’S safe to say crop breeding has never been more excit ing. Decades ago, breeders transformed the field by scaling up plot numbers and enabling wide-scale phenotype selection. Years later, genome mapping and new genetic tech nologies drove another shift. Now, AI is pushing crop breeding into its next phase, and the industry is paying attention. First, the big picture. In 2025 alone, researchers published numerous scientific reviews examining how AI is changing crop breeding. In one review, a team in India explains that “advances in genomics, phenomics and environmental sensing have ena bled the development of high-dimensional datasets, fostering more precise and efficient breeding strategies.” They note that AI-driven approaches, including machine learning models such as random forests and convolutional neural networks, improve phenotypic predictions and yield forecasting. Deep learning also accelerates genotype-to-phenotype mapping by extracting key traits from large-scale datasets. In June 2025, U.S. Department of Agriculture (USDA) scientists Worasit Sangjan, Daniel Kick and Jacob Washburn published another review. They highlight similar trends and add that AI supports crop breeding through data mining, multi-omics, environmental tracking, crop management practices, cross-species inference, sustainability and economics. Chris Reberg-Horton, North Carolina State University researcher, works with large-scale plant imaging datasets designed to train AI systems to recognize species, growth stages and key traits across environments. Photo: NC State
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