Digging Deep into Interdisciplinary Agricultural Research
Geospatial Analytics Ph.D. student and Sweet Fellow Randi Butler is starting research this fall on an interdisciplinary agricultural data science team using a variety of techniques to reduce agricultural waste and increase sweet potato yield.
New PoPS Border Simulation Supports Front Lines of Pest Detection
Software developers at the Center for Geospatial Analytics have released the first version of a pest or pathogen spread (PoPS) simulation that can quantify the effectiveness of inspections at ports of entry and reveal how many pests are missed.
Researchers Design Simulation Tool to Predict Disease, Pest Spread
A team led by Research Scholar Chris Jones developed a computer simulation tool called PoPS to predict pest and disease attacks on crops or forests, and to test when and where to apply pesticides or other management strategies to contain them.
GIS Problem-Solving Through Service Learning
During the Spring 2021 semester, our graduating professional master’s students applied their knowledge and skills in community partnerships that supported affordable housing, pollution prevention, local planning and more.
Where There’s Smoke: Reducing Downwind Impacts of Prescribed Burns
New research by Geospatial Analytics Ph.D. student Kate Jones will use geospatial modeling and interactive data visualization to help fire managers consider the vulnerability of communities downwind of prescribed burns.
Helping Clean Energy Move Forward
Geospatial Analytics Ph.D. student Alex Yoshizumi is combining passions for geography, planning and sustainability to help map North Carolina’s energy future.
First Official Version of PoPS (Pest or Pathogen Spread) Model Released
Developers at the Center for Geospatial Analytics have announced the first stable release of the Pest or Pathogen Spread model (PoPS 1.0), the Center’s signature open source system for forecasting the spread of insect pests and disease and for testing control strategies.
Protecting Our Future Food Supply with AI and Geospatial Analytics
In partnership with Lenovo, Associate Director of Spatial Computing and Technology Raju Vatsavai is applying artificial intelligence (AI) and deep-learning algorithms to the problem of global crop monitoring with the hopes of “optimizing the needs of future populations.”
Faculty Focus: Nelson to Help USDA Guide Agricultural Innovation
Faculty Fellow Natalie Nelson was selected by the US Department of Agriculture to serve as one of 11 subject matter experts for the USDA’s Agriculture Innovation Agenda. Her expertise in machine learning and data science will inform proactive strategies to prevent crop loss.
Plant Aid: A Big-Data Project to Detect Plant Diseases Faster
An interdisciplinary team including Center for Geospatial Analytics researchers will combine small sensors with big data for faster detection of the diseases plaguing tomato fields. From a hand-held plant disease "sniffer" to a cloud-based database that can alert farmers about the cause of the stress and suggest possible mitigation strategies, the project aims to detect diseases early, improving yield.