Robert Austin
Research and Extension Specialist - Geographic Information Systems
Williams Hall 3403F
919-513-0255 rob_austin@ncsu.eduBio
Specialty: Geographic Information Specialist
My work is directed toward geomatics and the application of related technologies in soils, agriculture, and the environment. I largely support faculty, staff, and students in the use of geospatial information and analytics.
Current program emphasis is directed toward (i) the use and application of unmanned aircraft systems (UAS) in early-season nitrogen management and crop scouting, (ii) the optimization of commercial nitrogen inputs through on-farm, multiple rate strip trials, and (iii) the spatial characterization of heavy metals on agriculture land resulting from the 2014 Dan River coal ash spill.
Other technology and programming interests include: web-based distribution of spatial data (RESTful APIs, leaflet, GeoServer, PostgreSQL PostGIS), open-source spatial solutions (GDAL/OGR), mobile application development, spatial analytics (R), and the development of tools and services for use in agriculture and farm management
Publications
- A Canopy Height Model Derived from Unmanned Aerial System Imagery Provides Late-Season Weed Detection and Explains Variation in Crop Yield , Agronomy (2025)
- Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks , PLANT METHODS (2025)
- Drone-based polarization imaging system for leaf spot severity determination in peanut plants , PLANT PHENOME JOURNAL (2025)
- Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut , PLANT PHENOME JOURNAL (2025)
- Integration of multi‐omics approaches reveals candidate genes for drought stress in St. Augustinegrass (Stenotaphrum secundatum) , Crop Science (2025)
- Polarimetric peanut drone images of peanut crop infected with leaf spot , (2025)
- Polarimetric peanut drone images of peanut crop infected with leaf spot , (2025)
- Polarimetric peanut drone images of peanut crop infected with leaf spot , (2025)
- Predicting arsenic and manganese contamination in private well water with Machine Learning: An integrated analysis of geologic, well construction, and permitting data. , (2025)
- Predicting maize yield loss with crop-weed leaf cover ratios determined with UAS imagery , WEED SCIENCE (2025)