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...

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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
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Summary: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 seasons to explore LiDAR-based evaluation of plant height and lodging in spring wheat. A total of 90 plots of 30 historical wheat cultivars were planted in a Randomized Complete Block Design (RCBD), with LiDAR data collected at key growth stages. Four methods—height distribution vector, overhead projection, orthographic projection, and voxelized spatial grid—were assessed for height and lodging classification. For plant height, overhead projection (95.98%) and orthographic projection (96.05%) delivered the highest accuracy, with RMSE and MAPE at 3.7 cm and 3.55%. Voxelized spatial grid and height distribution vector methods showed lower performance, with R-squared values of 88.15% and 84.37%. For lodging, overhead projection excelled, achieving up to 0.97 Quadratic Weighted Kappa (QWK), and 98.51% Macro-F1 (MF1), especially in 2- and 3-class scenarios. The voxelized spatial grid performed well, while the height distribution vector and orthographic projection lagged with more classes. These results establish overhead projection as a robust method for high-throughput phenotyping in spring wheat.
ISSN:1712-7971

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