Papers
13
Total Citations
789
H-Index
7
About
Sierra Young is a pioneering researcher at the intersection of agricultural robotics, computer vision, and precision agriculture, whose work has fundamentally advanced how autonomous systems are applied to crop production challenges. Her highly cited 2020 survey on public datasets for computer vision in precision agriculture (364 citations) has become an essential reference for researchers navigating AI-driven agricultural applications. Young's contributions span the full technological stack of agricultural automation — from her early ground-breaking work designing a field-deployable phenotyping robot for energy sorghum (2018, 120 citations) to her deep transfer learning investigations enabling accurate multi-class weed identification in cotton systems (147 citations). Her research into robotic weeding systems, human-machine interfaces for unmanned platforms, and opportunities for cotton production automation collectively paint a vision of a fully autonomous agricultural future. Notably, Young's reach extends beyond crop fields into hydrology, applying low-cost robotics to water measurement in data-sparse regions. With nearly 600 cumulative citations across a focused and coherent body of work, she has established herself as an authoritative voice shaping both the scientific foundations and practical deployment of agricultural robotics technologies.
Research Focus
Key Achievements
Top Papers
- 1A survey of public datasets for computer vision tasks in precision agriculture364 citations · 2020
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- 5Opportunities for Robotic Systems and Automation in Cotton Production37 citations · 2021
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