Duration: 10/01/2026 to 09/30/2031

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

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 climate 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, 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 of researchers 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 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, climate adaptation, 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.

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

Related, Current and Previous Work

Objectives

1. Objective 1: Advance sensor technologies and data acquisition systems for plant phenotyping in controlled and field environments
   Comments: Rationale: Reliable and interoperable sensor systems are critical for multi-environment phenomics. Shared calibration and data acquisition frameworks will enhance reproducibility across sites and species. Sub-objectives: 1.1. Develop, refine, and validate multisensor and imaging platforms for diverse crops and environments, including underutilized and specialty species. 1.2. Establish calibration protocols and metadata standards, including testing of new sensors, to ensure comparability and data quality across species, institutions and environments. 1.3. Develop and disseminate best practices for metadata and data management following FAIR principles to enable data sharing and interoperability, utilizing collaborative infrastructure. 1.4. Develop open-source software and hardware interfaces for automated and high-throughput data collection. Expected outcomes: - Standardized calibration and acquisition protocols across multiple crops and environments. - Cross-platform data repositories and benchmark datasets. - Improved interoperability and reproducibility across major and minor crops. Proposed Group Research Project: NC1212 Sensor and Data Standards Consortium – A coordinated effort among member institutions to standardize calibration targets and reference datasets for shared use across field and controlled-environment platforms, which lead to an agreed-upon sensor calibration process across facilities.
2. Objective 2: Link phenotype to function through integrative, multiscale analyses
   Comments: Rationale: Understanding how observed traits relate to physiological, molecular, and genetic mechanisms is central to predicting and improving crop performance. Sub-objectives: 2.1. Integrate phenomic data with omics and environmental datasets to uncover mechanisms of productivity, stress tolerance, and nutrient efficiency. 2.2. Develop frameworks connecting trait dynamics across temporal and spatial scales to underlying plant functions. 2.3. Identify key measurable phenotypes serving as proxies for physiological and genetic processes in both major and minor crops. Expected outcomes: - Integrated datasets linking traits to underlying function. - Mechanistic understanding of plant performance and resilience. - Validated trait-based predictors applicable across crop species. Proposed Group Research Project: Phenotype-to-Function Network Pilot – Shared field and controlled-environment trials across NC1212 sites evaluating stress response phenotypes in both major crops and other crops or plant species using standardized sensor and sampling methods.
3. Objective 3: Develop and apply artificial intelligence/machine learning (AI/ML) approaches for decision support and predictive modeling of phenotypes
   Comments: Rationale: AI/ML approaches enable efficient extraction of knowledge from complex phenomic datasets, facilitating prediction, discovery, and translation to breeding and management. Sub-objectives: 3.1. Apply and compare ML methods (e.g., convolutional neural networks, ensemble models, transformers) for predicting plant traits and performance. 3.2. Develop explainable and transferable AI frameworks to enhance interpretability and link data-driven models to biological insight. 3.3. Create shared, open-source tools and benchmark datasets for AI-based phenomic data analysis and visualization. Expected outcomes: - Predictive models for trait estimation, stress response, and yield. - AI tools and datasets accessible to the broader phenomics community. - Enhanced understanding of how machine learning outputs map to biological mechanisms. Proposed Group Research Project: AI Challenge Dataset for Phenomics – A collaborative NC1212 initiative to create an open, curated dataset across sites and crops (including minor species) for benchmarking and community development of AI models for trait prediction.
4. Objective 4: Foster education, training, and outreach in plant phenomics and data science
   Comments: Rationale: Cross-disciplinary training is critical for building the next generation of scientists and professionals in phenomics and data-intensive agriculture. Sub-objectives: 4.1. Develop modular, open-access training materials in sensing, data management, and modeling for diverse audiences. 4.2. Organize exchange programs linking plant scientists, engineers, and data scientists. 4.3. Expand outreach and engagement with students, stakeholders, and underrepresented groups. Expected outcomes: - Network-wide training programs and resource-sharing. - Increased data literacy and technical proficiency. - Stronger interdisciplinary and inter-institutional collaboration . Proposed Group Research Project: NC1212 Phenomics Course Series – “Portable” phenotyping course sessions and modules developed and recorded by individuals at member institutions to contribute towards plant phenomics literacy, spanning field and controlled environment phenotyping, data pipelines, and AI modules.

Methods

Measurement of Progress and Results

Outputs

Outcomes or Projected Impacts

Milestones

Projected Participation

View Appendix E: Participation

Outreach Plan

Organization/Governance

Literature Cited

Attachments

Land Grant Participating States/Institutions

DE, KY, LA, MO, NC, NE, WA

Non Land Grant Participating States/Institutions

Danforth Plant Science Center