30 / SEEDWORLD.COM JUNE 2026 lished 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. “Improvements in these areas could increase predictive accuracy for plant traits,” they explain, “thereby expediting breeding 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, phe notypic and environmental data with greater precision. These models help breeders predict the genetic potential of future phe notypes and refine hybridization strategies. As a result, breeders can shorten breeding cycles and improve selection accuracy. Another review, published in July 2025 in Nature by an inter national team spanning the U.S., U.K., Germany, Saudi Arabia, Australia and China, outlines how breeders can integrate 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 germ plasm,” by identifying markers and by interpreting structure, plasticity, regulatory logic and environmental interactions. This level of “germplasm intelligence” supports predictive trait mod eling, optimized parental design and more targeted selection. 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 labor 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 pro vide 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 containing geneti cally 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 exploring whether ESGAN and similar tools can double breeding progress in perennial crops such as miscanthus by compressing pheno typing, 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 commercial ize the technology with the right opportunity. The group focuses on academic applications across multi-location crop trials and continues expanding AI-based computer vision tools for plant science. He says seed companies already use aerial remote sens ing widely, which could accelerate ESGAN’s adoption. 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 sys tems 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.” Esleyther Henriquez Inoa demonstrates field-scale phenotyping systems that capture large volumes of plant data, supporting AI-driven analysis across breeding trials. PHOTO: NC STATE
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