Raghav Khanna
Papers
10
Total Citations
1,293
H-Index
9
About
Raghav Khanna is a leading researcher at the intersection of robotics, computer vision, and precision agriculture, whose work is fundamentally reshaping how we monitor and manage crop systems. His primary research areas include autonomous robotic systems, agricultural informatics, and multi-modal perception for farming applications. Khanna's most impactful contribution is the development of UAV-based crop and weed classification systems for smart farming, a paper that has garnered 384 citations and established foundational methods for per-plant monitoring to reduce herbicide use. He further advanced the field with WeedMap (287 citations), a large-scale semantic weed mapping framework that combines aerial multispectral imaging with deep neural networks for precision intervention. His work on informative path planning (297 citations) introduced efficient sampling-based methods for robots to autonomously explore unknown environments, overcoming local minima challenges. Khanna also pioneered the Flourish project, building integrated aerial-ground robotics systems that combine UAV survey capabilities with UGV targeted intervention. His research has achieved over 1,200 total citations, demonstrating significant impact in sustainable agriculture technology.
Research Focus
Key Achievements
Top Papers
- 1UAV-based crop and weed classification for smart farming384 citations · 2017
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- 5AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming95 citations · 2019
- 6A spatio temporal spectral framework for plant stress phenotyping50 citations · 2019
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- 8Flourish - A robotic approach for automation in crop management13 citations · 2018
- 9On field radiometric calibration for multispectral cameras13 citations · 2017
- 10Building an Aerial-Ground Robotics System for Precision Farming.7 citations · 2019