Six projects selected for funding from Spring 2026 Leveraging AI to Advance Life Sciences and Agriculture RIR

Posted Jul 27, 2026

Following the event, 11 self-assembled teams submitted funding proposals. The six projects selected for investment bring together researchers from across the university to apply artificial intelligence and machine learning to challenges including plant genome engineering, drought resilience, precision livestock management, controlled-environment agriculture, renewable natural gas production, and conservation monitoring. Each RIR project will receive $60,000 in seed funding to generate preliminary results, establish proof of concept, and strengthen interdisciplinary collaborations. The awards are intended to position teams for larger-scale funding opportunities from federal agencies, industry partners, and private foundations.

AI-Enabled Next-Generation Delivery Technology for Plant Genome Engineering

Principal Investigator

Shan Jiang, professor, Materials Science and Engineering

Co-PIs

Wei Le, professor, Computer Science
Steve Whitham, professor, Plant Pathology, Entomology, and Microbiology
Juan Ren, associate professor, Mechanical Engineering

Key personnel

Fei Liu, research scientist, Materials Science and Engineering

Proposed project summary

Advances in CRISPR and other genome editing tools have dramatically expanded the potential for crop improvement, but a major bottleneck remains: efficiently delivering genetic material into plant cells without damaging them. Current delivery methods, particularly biolistic (i.e., “gene gun”) approaches show promise but still rely heavily on trial‑and‑error optimization.

This project aims to transform plant genome engineering by developing an AI‑enabled framework for next‑generation delivery systems. Building on a recent Iowa State innovations in biolistic technology—including a novel Flow Guiding Barrel (FGB) system—the team will integrate artificial intelligence across three key areas: optimizing particle delivery mechanics; mapping plant tissue properties; and designing delivery agents for CRISPR components. By linking these complex and interdependent variables, the project seeks to move beyond empirical experimentation toward a predictive, data‑driven approach.

“We hope this research will establish a new paradigm for plant genome editing technologies, accelerating innovation in agriculture for years to come,” said PI Jiang.

University Strategic Plan aspirational statements this project advances

To be a trusted partner for proactive and innovative solutions

Potential external funding partners and opportunities

National Science Foundation (NSF) Plant Genome Research Program (PGRP)
NSF Enabling Discovery Through Genomics (EDGE)
Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Plant Health and Production and Plant Products (PHPPP)
Department of Energy (DOE) Biological and Environmental Research (BER)
Advanced Research Projects Agency-Energy (ARPA-E)

AI-Guided Genomic Discovery of Cuticle-Mediated Adaptation to Enable Drought-Resilient Maize

Principal Investigator

Matthew Hufford, professor, Ecology, Evolution, and Organismal Biology

Co-PI

Marna Yandeau-Nelson, professor, Ecology, Evolution, and Organismal Biology

Key personnel

Corrine Grover, research scientist, Ecology, Evolution, and Organismal Biology
Keting Chen, research scientist, Genetics, Development, and Cell Biology

Proposed project summary

Drought remains one of the most significant threats to corn production, costing U.S. farmers millions of dollars annually and highlighting the need for crops that can better withstand water stress. While maize contains a wealth of untapped genetic diversity that could improve drought resilience, identifying the genes responsible for adaptation has proven difficult using traditional research approaches.

This project will develop an artificial intelligence framework that combines plant traits, genetic sequences, and environmental data to identify genes associated with drought adaptation in maize. Focusing on the plant cuticle—a protective outer layer that helps reduce water loss—the team will study diverse maize landraces and use AI to uncover genetic signatures linked to drought tolerance. The research aims to identify new targets for breeding more resilient corn varieties while establishing a model for using AI to discover adaptive traits that could help crops withstand a range of environmental stresses.

“This grant will begin a new collaboration between the Yandeau-Nelson and Hufford labs to explore how the plant cuticle has allowed maize to adapt to drought-prone regions,” said PI Hufford. “Approaches leveraging artificial intelligence will allow these groups to combine data types and gain power to detect genes underlying adaptation based on cuticular traits.”

University Strategic Plan aspirational statements this project advances

To be the university that creates opportunities and forges new frontiers
To be the trusted partner for proactive and innovative solutions
To be the most student-centric leading research university

Potential external funding partners and opportunities

USDA NIFA PHPPP
NSF Plant Genome Research Program
NSF Directorate for Biological Sciences

Digital Tools to Advance Animal Health, Production, and Welfare Through AI-Based Continuous Phenotyping

Principal Investigator

Juan P. Steibel, professor, Animal Science

Research team

David Rosero, assistant professor, Animal Science
James Koltes, associate professor, Animal Science
Dawn Koltes, assistant professor, Animal Science
Elizabeth Bobeck, associate professor, Animal Science
Anna Johnson, Morrill Professor, Animal Science
Anna Wolc, affiliate assistant professor, Animal Science
Edison Magalhaes, assistant professor, Animal Science
Gustavo Silva, associate professor, Veterinary Diagnostic and Production Animal Medicine
Santosh Pandey, professor, Electrical and Computer Engineering
Ye Bi, postdoctoral research associate, Animal Science
Mina Shumaly, PhD candidate, Genetics and Genomics

Proposed project summary

Monitoring the health, behavior, and welfare of individual livestock animals is time-consuming and difficult at the scale of modern production systems, limiting researchers’ and producers’ ability to quickly identify issues and improve performance. As animal agriculture increasingly adopts digital technologies, there is a growing need for reliable tools that can automatically track and monitor animals in real time.

Building on several years of collaborative research, this project will expand the team’s existing work to develop a fully automated, AI-powered system for identifying, tracking, and monitoring individual animals on research and commercial farms. The researchers aim to create a practical, farm-ready tool that can be deployed using existing infrastructure while minimizing the need for costly computing power, data storage, and transmission systems.

“This RIR funding allows us to dedicate effort to improve our existing tracking program and test it in several of our campus farms,” said PI Steibel. “The resulting data and system will allow us to attract federal and private funding for large-scale deployment.”

University Strategic Plan aspirational statements this project advances

To be the university that creates opportunities and forges new frontiers
To be the trusted partner for proactive and innovative solutions

Potential external funding partners and opportunities

USDA NIFA Animal Breeding and Genetics, Agricultural Genome to Phenome Initiative (AG2PI), Data Science, Critical Agriculture Research Extension (CARE), and Engineering programs
Breeding and technology companies

CyberCEA: AI-Driven Digital Twin for High-Value Medicinal Crop Production in Controlled Environment Agriculture – Saffron as a Pilot Crop

Principal Investigator

Ajay Nair, professor and chair, Horticulture

Research team

Baskar Ganapathysubramanian, professor, Mechanical Engineering and director, AI Instititute for Resillient Agriculture
Soumik Sarkar, professor, Mechanical Engineering and director, Translational AI Center (TrAC)
Anuj Sharma, professor, Civil, Construction and Environmental Engineering and associate director, TrAC

Proposed project summary

High-value medicinal and specialty crops can provide farmers with new revenue opportunities, particularly when grown in controlled indoor environments that enable year-round production. However, growers often lack the data and predictive tools needed to determine the environmental conditions that maximize crop quality, yield, and profitability.

This project will use cilantro, saffron, and herbs as a model crops to develop an artificial intelligence-driven framework for indoor crop production. Researchers will collect plant-growth data across a range of environmental conditions and use them to build a predictive digital twin capable of simulating crop performance. While cilantro and saffron are the initial focus, the AI protocols and digital-twin technology are designed to be transferable to other high-value crops, such as basil, chives, oregano, ginger, and turmeric, creating new opportunities for year-round production and diversified farm income.

“Hydroponic crop production represents one of the fastest-growing sectors of controlled environment agriculture,” said PI Nair. “Our team of engineers and horticulturalists is grateful for this RIR award, and we look forward to collaborating with industry partners to ensure our research has meaningful impact.”

University Strategic Plan aspirational statements this project advances

To be the university that creates opportunities and forges new frontiers
To be the trusted partner for proactive and innovative solutions
To be the university that fosters lifelong learning

Potential external funding partners and opportunities

USDA NIFA Smart and Connected Agriculture
NSF Plant Genome Research Program
NSF Cyber-Physical Systems
National Institutes of Health (NIH) National Cancer Institute (NCI)
DOE AI for Science
Industry partners, including Percival Scientific, LettUs Grow, Saffron Bio and John Deere Ventures

ACRE: Autonomous Closed-loop Reactor for AI-accelerated Anaerobic Digestion

Principal Investigator

Jean-Philippe Tessonnier, professor, Chemical and Biological Engineering

Co-PIs

Elmin Rahic, research scientist, Bioeconomy Institute
Baskar Ganapathysubramanian, professor, Mechanical Engineering and associate director, Translational AI Center

Proposed project summary

Anaerobic digestion is one of the most promising technologies for converting agricultural, food, and municipal waste into renewable energy and nutrient-rich fertilizer products. However, optimizing these systems is slow and labor-intensive: the microbial communities that drive digestion are highly sensitive to feedstock composition and operating conditions, and they respond over days to weeks.

This project will develop ACRE, Iowa State’s first self-driving laboratory for bioprocesses. By combining automated experimentation with artificial intelligence and machine learning, ACRE will run digestion experiments, analyze the results, and select the next set of operating conditions in a continuous closed loop, dramatically reducing the time needed to identify the most effective approaches. The team will initially apply the platform to the co-digestion of swine manure and meat-processing waste, with the goal of increasing biogas yields and the quality of the renewable natural gas produced from it.

“Optimizing an anaerobic digester today means waiting weeks to learn the outcome of a single experiment, which is why so many systems run far below their potential,” said PI Tessonier. “ACRE will develop the infrastructure needed to compress years of trial and error into months, and give Iowa’s farms and food processors a practical path to turning their waste into energy and fertilizer.”

University Strategic Plan aspirational statements this project advances

To be the university that creates opportunities and forges new frontiers
To be the trusted partner for proactive and innovative solutions
To be the most student-centric leading research university

Potential external funding partners and opportunities

USDA NIFA Sustainable Agricultural Systems
USDA NIFA Agriculture Food Research Initiative (AFRI) Bioenergy/Biomanufacturing programs
DOE and NSF Genesis Mission AI for Science
BioMADE
NSF Chemical, Bioengineering, Environmental and Transport Systems
Various industry and foundation partners

Smarter Stream: Using AI and Remote Sensing to Measure Conservation Practice Impacts

Principal Investigator

Zhengyuan Zhu, professor, Statistics

Co-PIs and core staff

Brian K. Gelder, research manager, Agricultural and Biosystems Engineering
Josh Obrecht, research scientist, GIS Facility
Matt D. Stephenson, research scientist, Bioeconomy Institute
Robert Gunzenhauser, associate professor of practice, Agronomy
Guimin Zheng, assistant professor, Political Science

Research team

Sadegh Jafari, PhD student, Computer Science
Wael Alhaj, research scientist, GIS Facility
Colton Clack, GIS analyst, GIS Facility

Proposed project summary

Conservation practices such as cover crops, wetlands, and prairie strips are designed to improve water quality, but determining their effectiveness across entire watersheds remains a significant challenge. Monitoring data can be sparse and arduous to collect, while conservation practices vary across landscapes and over time, making it difficult for researchers and policymakers to quantify their impacts.

This research team will combine satellite imagery, conservation practice records, and existing water-quality data to develop an AI-powered system for evaluating real-time watershed health. The project will use machine learning and causal inference methods to better understand how conservation efforts influence indicators of water quality, such as turbidity and algal growth. By demonstrating a scalable, cost-effective approach to monitoring environmental outcomes over large geographic areas, the project could help guide future conservation investments.

“If successful, the project will provide a pathway to develop a scalable, data-driven approach to measure the real-world impacts of conservation practices across entire watersheds, which can give farmers, conservation agencies, and policy makers better evidence to make effective investment to protect our water resources,” said PI Zhu.

University Strategic Plan aspirational statements this project advances

To be the university that creates opportunities and forges new frontiers

Potential external funding partners and opportunities

NSF Smart and Connected Communities (S&CC), Environmental Sustainability, Growing Convergence Research (GCR), and Water, Landscape and Critical Zone Processes (WaLCZ)
USDA NIFA AFRI
National Oceanic and Atmospheric Administration (NOAA)
Great Lakes Protection Fund