2026 Projects

8. Predictive VOC Signatures for Biocontrol and Seedling Vigor

Microbial volatile organic compounds (VOCs) are emerging as powerful tools for sustainable agriculture. Advances in metabolomics now enable comprehensive profiling of plant and microbial metabolites using high-resolution techniques such as HS-SPME/GC-MS. This project leverages these innovations to identify VOC biomarkers produced by beneficial and pathogenic microbes on bean seeds and seedlings—providing early indicators of plant health and supporting proactive crop management. 

What You’ll Do 
Participants will gain hands-on experience in next-generation metabolomics and plant–microbe interaction research. You will: 

• Profile and quantify VOCs emitted by biocontrol microbes across developmental stages.
• Analyze metabolite dynamics and link them to seedling vigor and disease resistance.
• Integrate metabolite data with growth chamber assays, non-invasive imaging, and predictive modeling to uncover how microbial VOCs influence plant physiology, modulate defenses, and suppress pathogens. 

Why It Matters 
The integration of these datasets will enable the development of precision biomarkers for early prediction of seedling health and biocontrol efficacy—critical for sustainable crop management and food security. 

Fieldwork & Hands-On Training: 20%
Seed inoculation with biocontrol microbes, monitoring seedling growth and health, and conducting controlled growth chamber experiments. 

Data & Lab Analysis: 70%
VOC profiling using HS-SPME/GC-MS, metabolite quantification, non-invasive imaging, data integration, predictive modeling, and correlation with seedling health metrics. 

Professional Development: 10%
Scientific writing, preparing presentations, and participating in research meetings and extension events. 

Mentors: Collins Bugingo, Muhammad Ali Mumtaz

9. From Bud to Fruit: Developing AI-Driven Phenology Models in Grapevine

Join us this summer as we incorporate AI and robotic phenotyping into our grapevine breeding program. This project will use robotic imaging systems in greenhouse and vineyard settings to develop phenological machine learning models. Detecting phenological stages like budbreak, flowering, and veraison are critical to screen for varieties capable of avoiding spring frost damage without delaying harvest. As part of the Cornell Grapevine Breeding Program, the summer scholar will gain experience with image annotation, model development, viticulture, agricultural automation, and plant breeding. 

Field: 40%, Computer: 60%

Mentors: Aidan Inoue, Maddy Oravec

10. Advancing Accessible Precision Agriculture through Crop & Weed Imaging 

Join us this summer to push the boundaries of precision agriculture! You’ll capture high-quality images of crops and weeds across different growth stages and soil types and use this data to help develop discrimination models for small, autonomous, and accessible robotic weeding systems. Your work will help create smarter tools that make sustainable farming more efficient and achievable for everyone.  Two scholars will simultaneously assess the use of real world, cost-effective robot platforms and conduct this project in an interdisciplinary, collaborative manner. The two scholars will be jointly advised by Dr. Lynn Sosnoskie (Weed Science) and Dr. Yu Jiang (Digital Agriculture) along with support and guidance from graduate students and technicians from the two labs.

Field: 50%, Lab: 50%

Mentors: Lynn Sosnoskie, Yu Jiang, Jane Petzoldt, Rye Weber

11. Cracking the cuticle: How does crop growth rate and climate affect late russeting in Snapdragon™?

Snapdragon™ ‘NY1’ is an economically important premium apple variety grown in New York. A progeny of Honeycrisp, Snapdragon is known for its vibrant red color, super crunch, and spicy-sweet flavor with hints of vanilla. It has also gained strong regional visibility as the official apple of the NFL Buffalo Bills, contributing to its market identity and demand.

As summers become warmer and wetter, NY1 fruit often grows more quickly, and this rapid growth has been linked specifically to a fruit quality disorder called late-russeting. Late-russeting resembles traditional russeting (rough brown skin, like a potato) in that both involve disruptions to the fruit’s protective skin, but late-russeting lacks the corky periderm layer seen in classic russet. Fruit affected by late-russeting also seems to lose water more rapidly in storage, which can influence postharvest quality and marketability.

Despite its commercial significance, growers continue to face challenges with late-season cuticle disorders such as scarf skin and late russeting, which reduce packout and compromise fruit finish. These disorders appear associated with fruit growth rate and seasonal climate conditions, yet the mechanisms linking crop load, growth dynamics, and weather to cuticle failure remain poorly resolved. Understanding how these factors interact is essential for developing reliable, climate-resilient management strategies for NY1 growers.

The student working on this project will gain hands-on experience in orchards, learn to use sensors that track fruit growth, operate instruments that measure how much water apples lose, and use the R programming language to understand how weather and growing conditions affect fruit development.

Field: 70%, Computer: 30%

Mentors: Griffin Erich, Jason Londo

12. Hop Offspring Performance and Multi-trait Analysis for Prediction (HOPMAP)

You will help with phenotyping hop breeding lines for flowering time, branch length, and growth rate and integrating these measurements with historical datasets to identify parent-offspring relationships. By combining field-based phenotyping with historical performance, your work will uncover trait patterns that can inform selection and crossing decisions to improve hop breeding efficiency in New York.

Field: 75%, Computational Analysis: 25%

Mentors: Nathan Lewis, Larry Smart

13. Field to Fabric: Phenotyping Fiber Flax Breeding Populations for Cultivar Development

This project will give you a front-row seat to the future of sustainable fibers by characterizing Cornell’s fiber flax breeding lines. You will help phenotype diverse breeding populations — tracking emergence, height, flowering time, and stem traits that shape flax’s potential as a regional textile crop. Through hands-on fieldwork, you will learn the fundamentals of field phenotyping and data management while contributing to the development of improved fiber flax cultivars for New York agriculture.

Field: 80%, Greenhouse/Lab: 20%

Mentors: Claire King, Larry Smart

14. Space Saving Apple Trees

Although most apple trees grow a number of branches (left picture), columnar apple trees are characterized with little branching (right picture), thereby requiring less growing space and limited pruning in orchard. Join us to uncover the molecular pathways that regulate the columnar growth, while learning basic techniques in plant genomics, such as DNA and RNA isolation, DNA sequencing, and gene expression analysis. 

Lab: 80%, Field/Greenhouse: 20%

Mentors: Laura Dougherty, Kenong Xu

15. Apple Trees Grow Downward Branches

Apple trees grow upward. However, apple trees can grow their branches downward as well occasionally. Can we learn something from such rare growth habit in apple trees? The answer is YES, as understanding the underlying genetics and genomics will provide useful information for developing apple cultivars of ideal tree architecture. The mystery of downward growing apple trees is being revealed, but much more remains to be learned. 

Lab: 80%, Field/Greenhouse: 20%

Mentors: Laura Dougherty, Kenong Xu

33. Reprogramming Microbes: A Sustainable Approach to Crop Protection

With chemical fungicides losing effectiveness and harming ecosystems, sustainable alternatives are essential. This project pioneers next-generation biocontrol by converting plant pathogens into beneficial root colonizers that suppress disease naturally.” 

A defining feature of the project is training in integrative, next-generation technologies that are reshaping modern agriculture. Students will learn how multi-omics approaches can be combined to decode complex host–pathogen interactions and guide rational biocontrol design. By integrating molecular insights with greenhouse disease phenotyping, participants will master multi-omics techniques to decode host–pathogen interactions and apply these insights to design safe, effective biocontrol strains. This data-driven framework mirrors how modern biological products are developed and optimized for consistency, performance, and scalability. 

Fieldwork & Hands-On Training: 20%  
Collecting pathogen infected sample, field trail and documenting plant growth across field sites

Data & Lab Analysis: 70%  
Gene expression, Vector construction, DNA extraction, RNA extraction, Vector construction, Transformation, Sanger Sequencing, TurboID mapping 

Professional Development: 10% 
Scientific writing, preparing presentations and participating in research meetings, extension events

Mentors: Collins Bugingo, Muhammad Ali Mumtaz 

34. Foliage Aesthetics in Baby Brassicas

This summer, you will delve into the foliar morphology of Tuscan kale, curly kale, and collards. You will investigate these Brassica market classes to drive innovation of new, promising leafy green crops. Time spent in the greenhouse will focus on analyzing foliar leaf color, structure, and texture, while laboratory research will determine how these features can affect the leaf’s taste, quality, and nutritional components. Assessing variation in segregating populations will also provide foundational insights into new future crops for consumer desirability. You will be working with Graduate Student Maria Mott with guidance from Professor Phillip Griffiths. 

Greenhouse: 40%, Lab: 50%, Field: 10%

Mentors: Maria Mott, Phillip Griffiths