An Orchestration Engine for Scalable, On-Demand AI Phenotyping from UAS Imagery in Agriculture
Year: 2025
Type: conference-paper
Abstract: Farmers, worldwide, are increasingly leveraging data derived from Unmanned Aerial Systems (UAS) imagery to enhance decision-making. This trend reflects a significant rise in both academic and commercial sectors. Furthermore, advancements in UAS and battery technology now enable longer flights and greater coverage, while higher-resolution optical and multispectral cameras provide finer ground sampling distances (GSD). From a regulatory standpoint, the Federal Aviation Administration (FAA) is beginning to accommodate multi-drone operations and Beyond Visual Line of Sight (BVLOS) operations, which can ultimately reduce the cost to collect UAS imagery.
Authors: Lucas Waltz, Sarikaa Sridhar, Ryan Waltz, Paul Rodriguez, Chaeun Hong, Armeen Ghoorkhanian, Nicole DiMarco, Raghu Machiraju, Sami Khanal
Institutions: The Ohio State University, University of California San Diego
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4417283678