the USDA NIFA AFRI (Grant Number 2022- 67021- 36467 to N.F.), and by the Bellwether Foundation. Conflicts of Interest Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending to Donald Danforth Plant Science Center. Data Availability Statement Code and data associated with this manuscript are available on GitHub (https://github.com/danforthcenter/teff-manuscript).References Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S. Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia, and Implications for Bioavailability.” Journal of Food Composition and Analysis 20, no. 3: 161–168. Ass
Open resource ↗danforthcenter/teff-manuscript · pdf-raw-page:8 lines:1-98Unverified paper record
Quantifying Growth and Lodging in Tef ( Eragrostis tef ) With Uncrewed Aerial Systems (UAS)
Plant Direct · 1 May 2026 · 10.1002/pld3.70173
Abstract
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Plant phenotyping relevance
UAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
abstractWe compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost.
Code and data availability
The paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.
yzing images of plants (Gehan et al. 2017; Schuhl et al. 2026) that provides a framework for measuring and storing observations extracted per object within each image. All code associated with these analyses is available on GitHub (https://github.com/danforthcenter/teff-manuscript), as well as the PlantCV- Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho- mosaic (Figure 1A), tef plots were planted under power lines in the field, which could not be flown under due to UAS safety re- strictions. Pixels belonging to powerlines needed to be removed to measure plot heights. During import, PlantCV- Geospatial was used with a height percentile t
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