Global agriculture faces an unprecedented challenge to sustainably feed, clothe, and fuel a projected population of nearly 10 billion people by 2050 while reducing inputs, protecting ecosystems, and adapting to weather variability. To achieve this, we must accelerate the development of crops that deliver higher yields, improved nutritional value, and enhanced resilience to environmental stressors under increasingly constrained resource conditions. Addressing these challenges requires an integrated understanding of how plant genotypes interact with their environments and management practices—knowledge that can only be gained through precise and scalable measurement of plant traits across biological, spatial, and temporal scales.

Over the past two decades, plant genomics has undergone a revolution. Advances in next-generation sequencing technologies have made it possible to rapidly and affordably characterize the genetic makeup of thousands of individuals across hundreds of species. Yet, despite the massive expansion in genomic data, the capacity to accurately and efficiently measure phenotypes—the observable outcomes of genotype × environment × management (G×E×M) interactions—has lagged far behind. This “phenotyping bottleneck” remains one of the greatest limitations to progress in crop improvement, particularly under stressful environmental conditions where dynamic responses over time are key determinants of performance.

The Phenotyping Bottleneck and the Need for Integration

Traditional plant phenotyping approaches rely heavily on manual, labor-intensive measurements, limiting both throughput and accuracy. Even in advanced breeding programs, traits are often measured at a single timepoint and under a limited range of environments. As a result, most selection decisions are based primarily on yield—a complex, low-heritability trait—rather than on physiological or morphological features that more directly reflect adaptive capacity.

The rise of high-throughput phenotyping (HTP) and plant phenomics offers a transformative opportunity to overcome this bottleneck. Advances in imaging, spectroscopy, robotics, and automation now enable continuous, non-destructive measurement of plant traits at scale. These tools allow researchers to capture complex, dynamic processes—such as canopy development, water and nutrient use efficiency, and stress response—in real time. However, fully realizing the potential of these technologies requires coordinated efforts in data acquisition, standardization, analysis, and interpretation across research sites, crop species, and disciplines.

Convergence of Technologies: Sensing, Modeling, and AI

The next frontier in plant phenomics is the integration of advanced sensing with artificial intelligence (AI)/machine learning (ML) and data-driven modeling. These computational approaches can extract biological meaning from vast, multidimensional datasets, enabling predictive frameworks that link spectral, morphological, physiological, and molecular traits to yield and resilience outcomes. Yet, the field faces several challenges:

• Heterogeneity in data collection protocols and calibration across sensors and platforms;


• Limited interoperability and metadata standards, impeding data sharing and reuse;


• Gaps in translating sensor-based and computational outputs into biological understanding; and


• A shortage inpipeline of researchers and young talent trained to work effectively at the intersection of plant science, engineering, and data science.

To address these challenges, NC1212 provides a coordinated regional and national platform for developing standardized, scalable, and interpretable phenomics pipelines. These pipelines will link trait measurement to underlying mechanisms (“phenotype to function”) and apply predictive modeling for breeding and management decision-making.

Expanding Impact: From Major Crops to Minor and Underutilized Species

While most phenomics research has focused on major food crops such as maize, soybean, wheat, and rice, expanding these methods to minor and underutilized crops—including small grains, specialty crops, and regionally important and native species—is critical for agricultural diversification and efficient production systems. Such crops often lack the genomic and phenotypic resources available for widely cultivated species but play vital roles in nutrition security, local economies, and ecosystem services. By adapting and validating phenotyping platforms across diverse species and growth habits, NC1212 will broaden the reach and impact of phenomics technologies, supporting the USDA’s strategic goals for sustainable intensification and equitable agricultural innovation.

The NC1212 Approach: Collaborative, Multidisciplinary, and Applied

The NC1212 project brings together plant biologists, breeders, engineers, computer scientists, and data analysts to collaboratively advance the field of plant phenomics. Building on a history of successful cooperation among member institutions, the project will focus on four integrated objectives: (1) developing and standardizing sensor and data systems; (2) linking biophysical traits or phenotypes to physiological function; (3) applying AI/ML for predictive modeling of phenotypes; and (4) enhancing plant phenomics education and training.

Each objective is supported by tangible, group-based research initiatives:

• The NC1212 Sensor and Data Standards Consortium to harmonize data acquisition protocols across field and controlled environments;


• The Phenotype-to-Function Network Pilot to coordinate cross-site trials linking sensor data with physiological and omics measurements in both major and minor crops;


• The AI Challenge Dataset for Phenomics to generate open, standardized datasets for training and benchmarking predictive models; and


• The Phenomics Course Series to expand interdisciplinary training and outreach.

These coordinated efforts will create a foundation for reproducible, scalable, and interpretable plant phenomics research that accelerates discovery, translation, and application across crops, environments, and institutions.

Expected Impact and Alignment with USDA Priorities

The outcomes of this project will directly contribute to USDA and NIFA priorities for sustainable agriculture, adaptation to changing environmental conditions, and biosecurity. Specifically, NC1212 will:

• Advance understanding of G×E×M interactions to improve crop resilience and productivity;


• Provide validated, transferable tools for data-driven breeding and management;


• Enhance workforce development through cross-disciplinary education and training;


• Expand the scope of phenomics to include minor and specialty crops; and


• Strengthen data infrastructure for long-term, multi-site collaboration.

By integrating advanced technologies, fostering training and outreach, and coordinating multi-institutional research, NC1212 will enable a new era of predictive, systems-level plant science—linking sensors to function, and data to decision-making for a more sustainable and resilient agricultural future.

The project addresses the current USDA research and development priority ‘Increasing Profitability of Farmers and Ranchers’ by creating technologies, tools, knowledge, traineeship programs, and products to assist in producing better crops for the farmers. This may in turn impact other USDA priorities such as expanding markets and creating new uses of U.S. agricultural products, promoting soil health to regenerate long-term productivity of land, and improving human health through precision nutrition and food quality.

Expected participating states: NE, MI, IN, MN, WA, SD, DE, NC, KY, AZ, WY, MO, LA

Expected non-land grant participating institutions: Danforth Plant Science Center