34 SEEDWORLD.COM/CANADA JULY 2026 “Improvements in these areas could increase predictive accu racy for plant traits,” they explain, “thereby expediting breed ing cycles and optimizing resource management.” Earlier in 2025, a team in China reported that combining crop big data with AI allows researchers to model genomic, phenotypic and environmental data with greater precision. Another review, published in July 2025 in Nature by an international team spanning the U.S., U.K., Germany, Saudi Arabia, Australia and China, outlines how breeders can inte grate elite alleles generated through these technologies into both existing and newly domesticated crops. A team in China introduced the Breeding 5.0 framework. The framework positions AI as a tool that can “understand germplasm,” by identifying markers and by interpreting struc ture, plasticity, regulatory logic and environmental interactions. This level of “germplasm intelligence” supports predictive trait modeling, optimized parental design and more targeted selec tion. One of its key pillars is peopleless data capture. Automated phenotyping in trial plots, often referred to as high-throughput phenotyping platforms, speeds data collection, reduces labour and improves consistency. Imaging Every Plot, Every Trait To push phenotype selection further, AI systems rely on detailed image data from field plots. Breeders train these systems to detect specific traits and identify phenotypes of interest. University of Guelph researcher Riley McConachie uses AI for wheat head detection and identifying fusarium head blight. He explains that traditional field scoring of traits such as disease levels, leaf angle and head size takes significant time and often introduces subjective error. In contrast, “AI analysis tools provide opportunities to significantly decrease the time required to evaluate these characteristics, either by solving the problem directly or by supporting the evaluation process.” Drones, Robots and Field-Scale Data Researchers at Iowa State University developed a thin “phe norobot” that moves between rows and autonomously captures images. It collects high-quality data while navigating around plants. Other teams use similar robotic systems, while drones and satellite imagery continue to expand coverage at scale. In April 2025, a team at the University of Illinois Urbana- Champaign published a study describing an AI vision system that identifies flowering traits using drone imagery. The system analyzed “heading” across thousands of plots contain ing genetically diverse populations of two miscanthus species. It identified heading nearly nine times faster than in-field human observation. The team developed a custom learning approach called ESGAN. This method reduced the need for human-annotated training data by one to two orders of magnitude compared to traditional supervised learning. Researchers are now explor ing whether ESGAN and similar tools can double breeding progress in perennial crops such as miscanthus by compressing phenotyping, selection and crossing into a single season. “That would be a substantial gain,” University of Illinois Urbana-Champaign Center for Digital Agriculture’s Andrew Leakey says. Leakey says the team has filed a patent and may commer cialize the technology with the right opportunity. Reducing Human Bias in Phenotyping Echoing these points, Chris Reberg-Horton at North Carolina State University emphasizes that data preparation remains the biggest bottleneck. His team, working with the USDA Agricultural Research Service, is completing a large-scale AI crop imagery project called the Ag Image Repository (AgIR). The dataset includes 1.5 million plant images and trains AI systems to recognize species and growth stages. The team plans a nationwide release this fall. “Obtaining quality images, processing and annotating them is where the bulk of the work occurs,” he says. “When we get a new grant or contract, 95% of the effort goes into these steps.” He credits team members with automating that workflow. Kutugata notes that agriculture has lacked the large, well- labeled image datasets common in other industries. “AgIR closes that gap so we can train models that hold up across farms, seasons and applications,” he says. Because the dataset will remain open, researchers, students, small labs and growers can build and test tools without starting from scratch. University of Guelph researcher Riley McConachie uses AI for wheat head detection and identifying fusarium head blight.
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