Precision phenotyping in wheat: LiDAR-based plant height estimation and lodging classification using uncrewed ground vehicles

Precision phenotyping in wheat: LiDAR-based plant height estimation and lodging classification using uncrewed ground vehicles

Accurate phenotyping of wheat traits is essential for advancing breeding programs and crop science research. This study employs the University of Saskatchewan Field Phenotyping Systems (UFPS), an uncrewed ground vehicle (UGV), across six Canadian research stations during the 2023 and 2024 field seas...

Full description

Saved in:
Bibliographic Details
Main Authors: Prabahar Ravichandran, Keshav D. Singh, Scott D. Noble, Raju Soolanayakanahally, Jatinder S. Sangha, Elizabeth K. Brauer, Oscar Molina, Kirby T. Nilsen, Harpinder S. Randhawa, Keith Halcro, Clare Workman, Shankar Pahari
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Canadian Journal of Remote Sensing
Subjects:
high-throughput wheat phenotyping
deep learning
plant height estimation
lodging detection
uncrewed ground vehicle (ugv)
Online Access:http://dx.doi.org/10.1080/07038992.2025.2516742
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items