UAV-based high-throughput phenotyping in legume crops

Sindhuja Sankaran; Lav R. Khot; Juan Quirós; George J. Vandemark; Rebecca J. McGee

doi:10.1117/12.2228550

17 May 2016 UAV-based high-throughput phenotyping in legume crops

Sindhuja Sankaran, Lav R. Khot, Juan Quirós, George J. Vandemark, Rebecca J. McGee

Proceedings Volume 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping; 98660G (2016) https://doi.org/10.1117/12.2228550
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

In plant breeding, one of the biggest obstacles in genetic improvement is the lack of proven rapid methods for measuring plant responses in field conditions. Therefore, the major objective of this research was to evaluate the feasibility of utilizing high-throughput remote sensing technology for rapid measurement of phenotyping traits in legume crops. The plant responses of several chickpea and peas varieties to the environment were assessed with an unmanned aerial vehicle (UAV) integrated with multispectral imaging sensors. Our preliminary assessment showed that the vegetation indices are strongly correlated (p<0.05) with seed yield of legume crops. Results endorse the potential of UAS-based sensing technology to rapidly measure those phenotyping traits.

Citation Download Citation

Sindhuja Sankaran, Lav R. Khot, Juan Quirós, George J. Vandemark, and Rebecca J. McGee "UAV-based high-throughput phenotyping in legume crops", Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 98660G (17 May 2016); https://doi.org/10.1117/12.2228550

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available