Cornell University

06/17/2026 | Press release | Distributed by Public on 06/17/2026 08:54

13 student projects earn CIDA Research Innovation Fund awards

Thirteen Cornell students will spend the summer advancing new technologies for agriculture after receiving 2026 Research Innovation Fund (RIF) awards from the Cornell Institute for Digital Agriculture (CIDA).

The annual summer program supports student-led research projects that bring together expertise from agriculture, veterinary medicine, engineering, computing and data science to address challenges facing food and agricultural systems. Together, the projects reflect CIDA's mission to develop digital technologies that improve agricultural productivity, sustainability and resilience.

"These projects demonstrate how Cornell students are combining advances in AI, robotics and data science with deep agricultural expertise to develop practical solutions for farmers and food systems," said Fengqi You, Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in Cornell Duffield College of Engineering and co-director of CIDA. "The summer awards give students an opportunity to lead independent research projects while building interdisciplinary collaborations with faculty mentors."

The 2026 cohort includes 10 graduate student projects and three undergraduate projects representing students and faculty from the College of Agriculture and Life Sciences (CALS), Cornell Duffield College of Engineering (COE), Cornell Ann S. Bowers College of Computing and Information Science (CIS), and the College of Veterinary Medicine (CVM).

Addressing challenges facing agriculture in New York and around the world, each project pairs students with faculty mentors from multiple disciplines and focuses on topics ranging from AI-powered robotics and dairy herd health to environmental monitoring and precision agriculture. The awards provide summer funding of up to $11,500 for student-led interdisciplinary research projects.

Running throughout summer 2026, the projects provide students with hands-on experience developing technologies that could help shape the future of agriculture while strengthening collaborations across Cornell's colleges and disciplines.

2026 RIF awardees

Focus Area: AI and Robotics in Specialty Crops

These projects explore how artificial intelligence, computer vision and robotics can help automate labor-intensive agricultural tasks, from harvesting fruit to managing crop diseases.

Syed Usama Bin Sabir (Doctoral Student, CALS)
Project: Soft Robotic Flower Thinning for Scalable and Sustainable Orchard Management
Primary advisor: Manoj Karkee (CALS)
Secondary advisor: Preston Davis Culbertson (CIS)

Qianxu Wang (Doctoral Student, CIS)
Project: Learning Contact-Aware Dexterous Manipulation Policies for Robust Apple Harvesting
Primary advisor: Kuan Fang (CIS)
Secondary advisor: Manoj Karkee (CALS)

Divya Rathore (Doctoral Student, CALS)
Project: Perception-centered Human-Robot Collaboration for Robotic Apple Harvesting
Primary advisor: Manoj Karkee (CALS)
Secondary advisor: Max Zhang (COE)

Weilong He (Doctoral Student, CALS)
Project: Spatially Grounded AI-Guided Robotic Intervention for Tomato Disease Management
Primary advisor: Lirong Xiang (CALS)
Secondary advisor: Kuan Fang (CIS)

Focus Area: AI and Robotics in Agriculture

These projects explore ways to improve artificial intelligence, computer vision, and robotics in precision farming operations.

Yunhao Cao (Doctoral Student, CIS)
Project: Generalizable Agricultural Robot Foundation Models with Unified Task Representations
Primary advisor: Kuan Fang (CIS)
Secondary advisor: Lirong Xiang (CALS)

Ruiming Du (Doctoral Student, CALS)
Project: Using Vision-Language-Action Models to Create High-Fidelity 3D Agricultural Scenes for Robotics Co-Design
Primary advisor: Yu Jiang (CALS)
Secondary advisor: Wei-Chiu Ma (CIS)

Kenneth Kniffin '29 (Undergraduate Student, COE)
Project: Autonomous Weeding Robot for Sustainable Agriculture
Primary advisor: Lirong Xiang (CALS)
Secondary advisor: Matt Reid (COE)

Focus Area: Animal Health and Livestock

Focused on dairy production and animal well-being, these projects use data analytics and monitoring technologies to improve herd health, reduce disease risk and support more sustainable livestock management.

Amelia Nelson (Doctoral Student, CVM)
Project: Predicting Mastitis Risk from Milk Parlor Data and Positioning Patterns
Primary advisor: Renata Ivanek (CVM)
Secondary advisor: Julio Giordano (CALS)

Karrigan Ellison (Doctoral Student, CALS)
Project: Ammonia Emissions Monitoring in Dairy Calf Facilities
Primary advisor: Jason Oliver (CALS)
Secondary advisor: Taika von Konigslow (CVM)

Focus Area: Environmental Monitoring and Sustainability

These projects develop new tools for tracking crop health, soil conditions and agricultural emissions, helping farmers make more informed decisions while reducing environmental impacts.

Smaran Panth Kulakarni (Doctoral Student, COE)
Project: NMR and ML-based DMC Determination and Disease Detection in Tubers
Primary advisor: Amal El-Ghazaly (COE)
Secondary advisor: Mike Gore (CALS)

Cynthia Zheng '29 (Undergraduate Student, COE)
Project: Engineering IoT Sensors for Soil Nutrient Optimization and N₂O Mitigation
Primary advisor: Rebecca Nelson (CALS)
Secondary advisor: Max Zhang (COE)

Focus Area: Sustainable Farming Systems and Data Science

These projects use engineering design and data analysis to help farmers manage crops more efficiently, understand changing agricultural landscapes and support long-term sustainability.

Katrina Davis (Doctoral Student, COE)
Project: Codesign for a Living Mulch Management Robot
Primary advisor: Nils Napp (COE)
Secondary advisor: Yu Jiang (CALS)

Dekyel Samdron '27 (Undergraduate Student, CALS)
Project: Understanding Agricultural Changes Through Data Patterns in Crop Diversity
Primary advisor: Laura Melissa Guzman (CALS)
Secondary advisor: Dan Kowal (CIS)

Henry C. Smith is the communications specialist for Biological Systems at Cornell Research and Innovation.

Cornell University published this content on June 17, 2026, and is solely responsible for the information contained herein. Distributed via Public Technologies (PUBT), unedited and unaltered, on June 17, 2026 at 14:54 UTC. If you believe the information included in the content is inaccurate or outdated and requires editing or removal, please contact us at [email protected]