The Center for Digital Agriculture is a catalyst for collaborative research projects across engineering and agricultural disciplines. Leveraging the strong tradition of team building for large long-term interdisciplinary research and education projects at the University of Illinois, CDA offered a competitive seed-funding program for new collaborative projects spanning two or more of the Center’s initial themes: Automation, Data, Animals and Crops, and People.
A second round of seed funding will be awarded next year. Those interested in applying should plan to attend the information meeting on December 6, 2019, for proposals due January 24, 2020.
Addressing Effects of Soil and Water Pollution on Health in Rural Communities and Farmer Families — Liudmila Sergeevna Mainzer; Aiman Soliman; Kenton McHenry; Maxwell Burnette; Zeynep Madak-Erdogan; Rebecca Smith
Advancing Deep Learning for Vision Based Agricultural Phenotyping and Monitoring — Alexander Schwing; Girish Chowdhary; Brian Diers; Nicolas Martin; Nathan Kleczweski; D.K. Lee; Adam Davis
Algorithms and Methods for Simplifying Autonomy for Field Robots — Katherine Driggs-Campbell; Sasa Misailovic; Sayan Mitra; Girish Chowdhary
Data Carpentry for Agriculture: Computer Science Basics for Illinois Farmers, Agronomists, and CCAs — Lindsay Clark; Dena Strong; Carolyn Butts-Wilmsmeyer; Neal Davis
Deriving Tillage Practices and Irrigation Area/Methods from National Agriculture Imagery Database using Deep Learning and Supercomputing — Adam Stewart; Jian Peng; Kaiyu Guan
Estimating Yield and Water-Quality Response Functions using On-Farm Precision Experimentation, Spatially-Intense Soil Sampling, and Hyperspectral Imagery — David Bullock; Praveen Kumar; Andrew Margenot; Nicolas Martin; Laura Gentry
Event-Based Decision Making for Distributed Food and Agriculture Systems — Luis Rodriguez; Richard Sowers; Jay Solomon
Expanding the Utility of a Multi-Locus and Multi-Trait GWAS Model — Alexander Lipka; Lindsay Clark; Aurelie Lozano; Naoki Abe
Intelligent Development of Illinois Soils: ACES-Beckman-Center for Digital Agriculture (ABC) Collaborative Research in Soil Health Diagnostics — Tony Grift; Michelle Wander; Carmen Ugarte; Martin Bohn; Iwona Dobrucka
Machine Learning to Detect Fertilizer Adulteration in Developing Countries — Hope Michelson; Andrew Margenot; Ranjitha Kumar
Towards an Operational Methodology to Map Crop Photosynthetic Capacity from Airborne Remote Sensing — Kaiyu Guan; Elizabeth Ainsworth; Sheng Wang
Using Computer Vision to Relieve the Crop Phenotyping Bottleneck — Andrew Leakey; Narendra Ahuja; John Hart
Virtual Farming Networks for Smallholders through Digital Communities of Trust — Adam Davis; Lav Varshney