Quantifying growth and lodging in Tef ( Eragrostis tef ) with Uncrewed Aerial Systems (UAS)

tef, lodging, UAS, drone, structure-from-motion, LiDAR, phenotyping 23 24

25 Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. 26 Semidwarf varieties have been developed with a goal to increase yield through reduced 27 lodging, but studying lodging susceptibility currently requires a labor-intensive, 28 imprecise, manual scoring method. Here we present workflows for analyzing tef stand 29 height from UAS sensors across time to both predict lodging later in the season with 30 early height and to measure the severity of lodging after a storm event. We compare 3D 31 point clouds generated by photogrammetry from RGB images with those generated 32 from LiDAR to estimate height, demonstrating that they produce similar results, despite 33 differences in cost. Stand height and lodging can both be accurately measured with low-34 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 2 cost UAS, reducing the need for manual measurements and increasing precision and 35 temporal resolution in plant breeding programs. 36

37 Tef (Eragrostis tef (Zucc.) Trotter) is a C4 grass generally considered tolerant to 38 drought, flooding, and pests both in the field and during grain storage (Bekele-Alemu & 39 Ligaba-Osena, 2023), and is a major staple cereal crop in Ethiopia (Tadele & Hibistu, 40 2021). In addition to favorable agronomic traits related to stress tolerance, consumer-41 facing traits, such as gluten-free grain with high nutritional value, are driving increasing 42 global interest in tef (Abebe et al., 2007; Assefa et al., 2011; Gebremariam et al., 2014; 43 Spaenij-Dekking Liesbeth et al., 2005). However, widespread adoption of tef is hindered 44 by low yields compared to other grain crops such as maize and wheat (Mihretie et al., 45 2022). Lodging, competition from weeds, grain shattering, and low productivity are 46 major limitations to tef production (Assefa et al., 2011). Like other cereals, lodging in tef 47 is exacerbated by high wind speeds, heavy rainfall, and agronomic practices that favor 48 top growth and often cause tef to lodge, a phenomenon in which the stem bends or 49 snaps permanently at the base, causing the plant to fall over (Ben-Zeev et al., 2020; 50 Berry et al., 2004; Merchuk-Ovnat et al., 2020). Lodging in tef has been reported to 51 reduce grain yield by up to 25% and affects the quality of both grain and straw (Assefa 52 et al., 2011; Ben-Zeev et al., 2020; Gebru et al., 2023; Zeid et al., 2012). 53 54 Semidwarf plants have been associated with lodging resistance, so plant height has 55 been a target for traditional breeding in many crops. In wheat and rice, lodging resistant 56 varieties were bred using spontaneous mutations, resulting in shorter, thicker stems 57 (Dalrymple, 1985; Hedden, 2003). Maize breeding and biotechnology efforts recently 58 developed new short-stature varieties to increase yields by reducing susceptibility to 59 lodging (Barten et al., 2022). In cultivated barley, lodging is known to correlate with stem 60 height and thickness (Haaning et al., 2020). Tef stand height and lodging resistance is 61 influenced by variety, row spacing, seeding rate, and more (Blösch et al., 2020; Tasew 62 et al., 2024; Wato, 2019). To accelerate the process of traditional breeding, Beyene et 63 al. (2022) developed CRISPR/Cas9-based genome edited tef lines with reduced plant 64 height and improved lodging resistance, tested in a controlled growth environment. 65 In order to evaluate lodging resistance and its relationship to stature for breeding 66 pipelines, researchers must accurately measure plant height. However, the current 67 standard methods for measuring plant height are time-consuming and manual (Sun et 68 al., 2018; Wang et al., 2018). Additionally, to associate variation in height with lodging 69 resistance, a lodging index must be calculated from a subjective scale and human 70 estimations for the percentage of a plot at each lodging score (Caldicott & Nuttal, 1979). 71 This method provides information about the overall extent of lodging across a plot, but 72 remains subjective and time-consuming. Another challenge in studying lodging is that 73 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 3 researchers must wait for an event like a heavy storm and return to the field to manually 74 score soon after, which may not always be possible. 75 High-throughput phenotyping provides new opportunities to accurately, precisely, and 76 efficiently quantify plant height and lodging with imaging and computational methods to 77 measure plants and plots (Pauli et al., 2016; Shi et al., 2016; Vergara-Díaz et al., 2016; 78 Wilke et al., 2019). Measurements made from images captured from Uncrewed Aerial 79 Systems (UAS) and satellites are becoming increasingly popular due to their accuracy 80 and reduced manual labor (Haghighattalab et al., 2016; Hoffmann et al., 2018; Jindo et 81 al., 2021). In particular, drones equipped with RGB (red-green-blue), multispectral, 82 hyperspectral, and/or LiDAR (Light Detection and Ranging) sensors have provided 83 researchers with measurements of plant height, lodging, canopy cover, plant 84 architecture, and more (Ayankojo et al., 2023; Barbedo, 2019; Matias et al., 2022). The 85 reduced labor needs not only provide more reproducible measurements, but the 86 opportunity to increase the frequency or number of measurements feasibly taken in a 87 growing season. Thus, rather than manual lodging estimations during crop growth as 88 well as after a weather event, lodging can be measured as the change in height before 89 and after an event, providing a more holistic and dynamic approach to plant 90 measurements over time. 91 Generating 3-Dimensional (3D) reconstructions of plants and plots has been shown to 92 provide useful information on plant height and other measurements, but there is a trade-93 off between sensor cost and accuracy. LiDAR uses laser pulses to measure distances 94 from the sensor to objects below, making it particularly useful for estimating plant 95 height, stand height, and canopy density (Swinfield et al., 2019; ten Harkel et al., 2019). 96 However, LiDAR sensors and the UAS required to carry their often heavy payloads are 97 generally more expensive than RGB cameras and their associated UAS, and data 98 analysis is often complex. RGB imaging can be used to generate 3D point clouds using 99 structure from motion analysis methods (Leberl et al., 2010), which may have reduced 100 accuracy compared to LiDAR (White et al., 2013). While these methods are exciting, it 101 is critical that new software tools and methods to analyze these datatypes are 102 accessible and reproducible so that other researchers can utilize the measurement 103 pipelines. Lack of usable software remains a well-documented challenge in the plant 104 phenotyping community (Lobet et al., 2013). 105 In this study, we compare manual measurements of plant height and lodging for a group 106 of semidwarf tef lines and their parental control with measurements obtained using a 107 UAS equipped with RGB and LiDAR sensors. We present reproducible, sustainably 108 maintained analysis pipelines for estimating height using digital elevation models and 109 the open-source software package PlantCV-Geospatial. UAS-based phenotyping 110 generated accurate measurements of stand height and lodging from both RGB and 111 LiDAR data. This confirmed that semidwarf tef lines were less likely to lodge during 112 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 4 vegetative growth, in extreme weather 113 events, or during the grain filling stage. 114 UAS with either RGB or LiDAR sensors 115 thus provide new opportunities for 116 researchers and farmers to evaluate plant 117 varieties for improved lodging resistance 118 without subjective, manual measurements. 119

120 Plant growth - Tef lines were grown at the 121 Donald Danforth Plant Science Center 122 Field Research Site in St. Charles, 123 Missouri, USA (38.848 N, 90.458 W) from 124 June to September 2023. Two wild-type 125 lines (cultivar Magna) and seven genome 126 edited, semidwarf tef lines were planted 127 each on a 12 m2 plot (4 m x 3 m) 128 replicated three times in randomized 129 complete block design (a total of 27 plots). 130 Plots 101, 102, 103, 901, 902, and 903 131 were wild-type (Figure 1B). Plots 201, 132 202, and 203 were sd-1-1 lines, previously 133 published (Beyene et al., 2022), and the 134 remaining five were lines generated 135 targeting tef orthologs of known dwarfing 136 genes. Row spacing was 30 cm and 137 seeds were planted in each row manually 138 using a salt shaker to give an estimated 1-2 cm spacing between plants. The distance 139 between plots was 2 m and between replicated blocks 3 m. Plants were irrigated with 140 sprinkler irrigation as needed and plots were kept weed-free by manual hoeing. The 141 field used for tef planting was preceded by soybean the previous year, and chemical 142 fertilizers were not applied during the growing season. A severe windstorm occurred on 143 July 29th, 2023, ~8 weeks after planting. Wind speeds reached almost 22 mph (35.4 144 kph), as compared to averaging less than 4 mph (6.4 kph) the day before the storm 145 (measured by a PheNode environmental sensor, Agrela Ecosystems, Inc.; Fig. S1). 146 Manual data collection - Stand heights per plot were measured during vegetative growth 147 at 6 weeks after planting (July 13, 2023). Stand height was measured from three 148 different locations in each plot by measuring the height of tef plants from ground level to 149 the top of the canopy. To do so, an A4 sized piece of paper was placed on top of 150 standing plants in a uniform section of the canopy, and measured from the ground to 151 Figure 1 - UAS orthomosaics allowed for clear plot delimitation. (A) RGB orthomosaic from July 17th, 2023. Note the powerlines (white lines) above the field. (B) DEM created from the point cloud during stitching of the orthomosaic. The powerlines were converted to no data values by PlantCV-Geospatial using a height threshold. (C) LiDAR from the same date produced point clouds for height calculation. Higher points are toward the red end of the scale. .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 5 the paper with a ruler. The process was repeated at three randomly selected positions 152 in the plot, but only for standing, non-lodged plants. No plant was bent in the process, 153 as they supported the lightweight paper. Lodging index was measured at 15 weeks after 154 planting, which was 2 weeks after the last UAS flight, as per Caldicott and Nuttal (1979). 155 In brief, plots were scored from 0 (no lodging) to 5 (flat plants) on a subjective severity 156 scale, and the percent of the plot with each score was estimated to calculate a final 157 lodging score (Caldicott & Nuttal, 1979). 158 UAS data collection - UAS images were acquired at 12 timepoints throughout the 159 growing season, roughly every 1-2 weeks from the middle of June to the end of August. 160 Images were captured with a DJI M600 UAS mounted with RGB (Sony) or LiDAR 161 (Phoenix) sensors, and flown at 80 m with a constant horizontal speed of 28.8 kph 162 above the ground for all flights. Front and side imaging overlap for RGB images was 163 70%. Image resolution for RGB orthomosaics is reported in Table S1. For LiDAR, the 164 pulse repetition rate was 700kHZ, the field of view was 90 degrees, with two returns per 165 pulse and an accuracy of ~2 cm. 166 RGB image analysis - Raw images were used to construct orthomosaics using the 167 photogrammetry software Agisoft Metashape (Agisoft LLC, St. Petersburg, Russia) 168 through the Data to Science (D2S) platform (Jung et al., 2024; Jung, M., B. G. Hancock, 169 Z. C. Qian, N. Zhuo, Z. Gong, J. S. Doucette, J. Jung., n.d.), which also created a digital 170 elevation model (DEM) representing height of pixels in meters above sea level in the 171 orthomosaic from a dense point cloud. The orthomosaic blending mode was Mosaic 172 with “Fill Holes” enabled, the point cloud quality was set to high, the align photos 173 accuracy was set to high, surface was DEM, and the software version of Agisoft 174 Metashape Professional was 2.1.3 build 18946. Both orthomosaics and DEMs were 175 georeferenced using ground control points with the QGIS Georeferencer tool (Dawson 176 et al., 2025). Georeferencing was done to a single timepoint as a reference rather than 177 to an absolute coordinate system. To do so, a reference orthomosaic was opened in the 178 main map canvas, and the second orthomosaic was opened in the Georeferencer 179 window. The August 10th flight was used as the reference orthomosaic because this 180 was the timepoint used to create shapefile plot boundaries. Ground control point 181 coordinates were entered by clicking on corresponding points in the two orthomosaics. 182 Transformation type was Projective, resampling method was Nearest Neighbor, and 8 183 ground control points per orthomosaic were used to calculate the transformation for 184 georeferencing. A shapefile was also made in QGIS to generate polygons around each 185 tef plot on August 10, and used on the remaining images after georeferencing. 186 To measure height, georeferenced orthomosaics and DEMs were cropped to the field 187 and opened in Python using the PlantCV-Geospatial package, which is a library of 188 Python tools for analyzing geospatial data. PlantCV is a free, open-source image 189 analysis package for analyzing images of plants (Gehan et al., 2017; Schuhl et al., 190 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 6 2025) that provides a framework for measuring and storing observations extracted per 191 object within each image. All code associated with these analyses is available on 192 GitHub (https://github.com/danforthcenter/teff-manuscript), as well as the PlantCV-193 Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed 194 in the orthomosaic (Fig. 1A), tef plots were planted under power lines in the field, which 195 could not be flown under due to UAS safety restrictions. Pixels belonging to powerlines 196 needed to be removed to measure plot heights. During import, PlantCV-Geospatial was 197 used with a height percentile threshold for filtering, so values above the threshold were 198 converted to no data values. We used a threshold of 0.995 (unitless, 0-1 range), which 199 was sufficient to remove powerlines in this field (Fig. 1B). 200 To obtain the stand heights of each plant pixel in the tef plots, pixel height needed to be 201 subtracted from the elevation of the soil with respect to the mean sea level. Because the 202 land across the field was not uniformly level, a single soil elevation could not be used for 203 all plots. However, the soil surrounding each individual tef plot was relatively flat (Fig. 204 1B). Therefore, shapefiles per plot included a perimeter of soil so height was measured 205 by the difference between plot and soil elevation (Fig. 1). For each plot, the soil 206 elevation was estimated to be the 1st percentile in the distribution of plot height (Fig. 2), 207 calculated using ranked pixel values from each plot’s DEM. This soil value (red) was 208 then subtracted from the 95th percentile (blue) in the height distribution of each plot, 209 which was estimated to represent an average canopy height (Fig. 2). 210 LiDAR data analysis - The 211 LiDAR data in LAS format was 212 imported into MATLAB (R2024a) 213 as a 3D point cloud. An image 214 acquired on July 17, 2023 was 215 selected as a reference for 216 defining the region of interest 217 (ROI) of the tef field. A 218 customized algorithm was 219 developed, allowing rotation and 220 precise definition of the ROI 221 boundaries by selecting the 222 upper-left and lower-right corner 223 points of the field. Based on prior 224 knowledge of tef plant height 225 ranges, a height threshold of 3 m 226 above the ground level was 227 applied to exclude objects and 228 noise exceeding this height, 229 Figure 2- (A) Stand height was calculated as the difference between top (95%, blue line) and bottom (1%, red line) percentiles from the point cloud distribution. (B) An example of the 3D point cloud showing surrounding soil pixels. (C) A slice inside of a plot in the point cloud showing the distribution of plant heights across the canopy. .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 7 such as power lines. Once the mask parameters were determined from the reference 230 data, they were applied to mask all other LiDAR datasets. 231 The ROI was partitioned into a grid consisting of 3 rows and 9 columns (Fig. 1A). This 232 configuration was chosen to ensure that each grid cell contained soil points. Within each 233 grid cell, points located at least 25 cm above the minimum elevation were identified as 234 the initial plant subset. The initial plant subsets from all grids were then combined to 235 form the entire initial plant point cloud. A density-based noise removal method was 236 applied to this point cloud using MATLAB’s pcdenoise() function and the refined data 237 was segmented into clusters of interest (i.e. plots). The edge mask for each plot was 238 generated and was subsequently applied to all ROIs in other LiDAR datasets. 239 We estimated the plant height using two different approaches. The first approach is 240 similar to the RGB images, soil elevation for each plot was estimated as the 1st 241 percentile of the plot height distribution. This soil elevation was then subtracted from the 242 95th percentile of the height distribution, which was used to represent the average 243 canopy height for each plot. 244 The second approach is to estimate stand height by mimicking the manual 245 measurement process, which involves placing a flat paper over the plant canopy and 246 measuring its height above the ground. To simulate this, 10 x10 cm squares were 247 overlaid to cover each plot, excluding the 30 cm boundary region to remove the 248 boundary effects. Within each square, the maximum height of the plant points inside the 249 square area were measured. The final stand height for each plot was then determined 250 as the average height of the top 80th percentile of these maximum square heights. 251 Statistics - Linear models, ANOVAs, and correlation coefficients were fit and estimated 252 using base R (version 4.4.2) (Posit team, 2025). Linear mixed-effects models were fit 253 using the R package nlme (Pinheiro et al., 2025) in an R environment running version 254 4.1.3. 255

256 Short tef lines are resistant to lodging after a high wind event 257 Shorter plant varieties have consistently been shown to be resistant to plant 258 lodging; thus, we hypothesized that semidwarf tef would both be shorter than wild-type 259 tef grown in the field, as well as have reduced lodging. Indeed, height measured 260 manually at a single date showed that semidwarf tef was significantly shorter than wild-261 type (F=12.573, p=0.00231). Semidwarf tef also had significantly lower manual lodging 262 scores at the end of the season than wild-type (F=112.29, p=3.63e-09). Statistics are 263 from an ANOVA including both genotype (wild-type or semidwarf) and line as factors. 264 Height estimated from RGB images approximates manual measurements 265 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 8 We next sought to determine whether height estimated with RGB imaging would 266 correlate to manually-collected measurements, and how additional timepoints of 267 measurement might add temporal insights. To do so, we used the open-source software 268 PlantCV-Geospatial to estimate height from DEMs at each date (see Methods for 269 details). Using plant area at the 95th percentile threshold and soil area at the 1st 270 percentile had the highest correlation to manual measurements, with a Pearson 271 correlation coefficient (R2) of 0.83 (Fig. 3A), and was used as the height calculation 272 parameters for all plots and timepoints. This supports previous research suggesting 273 RGB imaging from UAS is an effective method for measuring stand height, as expected 274 (Hassan et al., 2019; Matias et al., 2022). 275 276 Figure 3- Measurements of height estimated from either RGB images (A) or LiDAR data (B) 277 correlated with manual measurements. RGB-estimated height was also correlated with LiDAR-278 estimated height (C). 279 Height estimated from LiDAR more closely approximates manual measurements 280 Next, we hypothesized that LiDAR would also correlate to manual stand height 281 measurements. Unlike for RGB images, we did not collect LiDAR within a day of the 282 manual measurements (July 13), so we compared the closest timepoint from a flight 283 four days later (July 17). Despite the intervening days, LiDAR-estimated height 284 predicted manual height measurements accurately (R2=0.89, Fig. 3B). RGB-estimated 285 height from images collected on July 12th had a lower correlation to manual 286 measurements compared to LiDAR, despite being closer in time (Fig. 3A). 287 Importantly, while RGB compared to manual measurements had a slope of 0.985 288 (95% CI 0.803-1.168), LiDAR slope was significantly different from 1 at 0.764 (95% CI 289 0.651-0.877) (see intersection of red and black lines in Fig. 3B). The reduced slope of 290 the correlation between LiDAR and manual measurements suggests that either taller 291 plots were underestimated by LiDAR, or that shorter plants grew faster than taller plants 292 in the 4-day span between the manual measurements and LiDAR flight. Which of these 293 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 9 hypotheses explains the relationship cannot be determined from this dataset. When 294 comparing RGB and LiDAR measurements captured on the same date (July 17), we 295 found a strong correlation between measurements from both sensor types (R2=0.90, 296 Fig. 3C), suggesting they provide similar results despite different methods. The intercept 297 of the correlation between RGB and LiDAR does differ from 0, however (t=4.202, 298 p=0.00295) due to RGB analysis producing larger height estimates. Correlation 299 between rank order is significant (Spearman’s rho = 0.963, p = 2.41e-07) indicating that 300 either method produces measurements with utility for assessing relative height 301 differences between plots. 302 Because the method for estimating height manually involved averaging the height of a 303 paper laid on top of several places in a plot, we also analyzed the LiDAR data using a 304

based on this idea (see Methods for more detail). The plot heights as estimated 305 by the LiDAR “paper” method were well correlated with the plot heights estimated by the 306 LiDAR plant and soil threshold subtraction method (R2 = 0.986, t = 85.50, p < 2e-16). 307 The slope of the correlation is slightly, but significantly, larger than 1 (95% CI 1.024 - 308 1.072), indicating that the soil subtraction method produces larger height estimates at 309 larger height values than the “paper” method. 310 UAS confirms semidwarf tef lines are shorter than wild-type and more resistant to 311 lodging 312 Manually collected data confirmed that 313 semidwarf tef lines were more resistant to 314 lodging. We next tested if UAS methods 315 could provide efficient metrics for lodging, 316 rather than the labor-intensive and subjective 317 manual lodging scores. First, we 318 hypothesized that height at a single timepoint 319 after a weather event may be a suitable 320 metric for lodging, and that plots with greater 321 lodging scores would have reduced height. 322 After the storm, wild-type plots had lower 323 average heights compared to semidwarf 324 plots, and remained lower for the remainder 325 of the season (Fig. 4). There was a 326 significant linear correlation (p-value = 327 6.627e-07) between the manual lodging 328 scores and the UAS measurements of plot 329 height on August 31, which was the last RGB 330 Figure 4 - Wild-type plants (orange) were taller than semidwarf lines (blue) early in the season, but lodged more both after a storm event (red vertical line) and progressively towards the end of the season. Colored line represents average of all wild-type or semidwarf lines, respectively. .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 10 time point and the closest measurement temporally to when lodging was scored. 331 However, even pre-storm height predicted final manual lodging scores. The plot 332 heights from July 27, the midpoint of the growing season and last timepoint before the 333 storm, had a significant relationship (t-value = 6.439700, p-value < 0.0001) to end-of-334 season lodging scores using a linear mixed-effects model (Fig. 5A). This relationship 335 indicates that measurements extracted from UAS images at mid-season time points can 336 predict the best performing lines. This 337 suggests that while height after a weather 338 event may be an effective high-339 throughput measurement of lodging, 340 without additional context of size before 341 an event, it’s not possible to know if the 342

is due to starting size or the height 343 change. 344 For a more robust UAS 345 measurement of lodging, we measured 346 the change in height using DEMs from 347 RGB images before and after the high-348 wind event (Figure S1). This change in 349 height was strongly correlated to height 350 before the storm, suggesting taller plants 351 were more likely to lodge (R2=0.55, p-352 value = 1.03e-05; Figure 5B). This 353 measurement of change, rather than 354 absolute height, requires multiple 355 timepoints, but eliminates conflating 356 stand height with lodging, where a short 357 variety may be considered lodged if only 358 compared at one timepoint. We 359 hypothesize that height thresholds might 360 predict lodging (> ~0.75m) or continued 361 growth (< ~0.65m), but sample sizes are 362 too small to statistically test for more complex dynamics (Figure 5A). More data could 363 enable explicit fitting of a changepoint model to capture information that could be useful 364 for breeding to a specific height threshold. 365

366 Figure 5 - Taller plants experienced a greater decrease in height after a storm (A) and had a higher manual lodging score at the end of the season (B). There was variation in height among semidwarf lines (circles) but the shorter lines were more resistant to lodging. Color represents a replicated semidwarf line (n = 3). .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 11 Here, we present a method for using UAS sensor data, both RGB and LiDAR, to 367 construct 3D point clouds to estimate height of genetically variable plots of Eragrostis tef 368 across a growing season. Both methods for image analysis of point clouds estimate 369 height accurately as compared to manual measurements (Figure 3), but decrease 370 necessary human time, allowing for higher temporal resolution. By imaging at multiple 371 time points through the season, we were able to capture the dynamics of lodging after a 372 high-wind speed storm. Breeding for higher yield in tef requires adopting strategies for 373 reduction in loss from lodging, such as the development of semidwarf varieties. We 374 show that the modified lines used in this study are both shorter and lodge less, and that 375 early season height variance among the lines predicts the severity of lodging. 376 Our method uses average stand height to compare lines and replicates. While average 377 stand height of a plot is important, the distribution of stand heights within a plot is an 378 important indicator of plant growth and lodging that can be missed when only 379 considering a plot-level average. While a person may be able to make a small number 380 of manual stand height measurements for a plot, UAS imaging measures stand height 381 for every pixel, and thus is often a more accurate representation of the whole plot. In 382 this study, plant height was described as a singular measure, but the level of detail 383 available to extract from this 3D image data will be valuable in understanding growth 384 dynamics, plant health indices, and more. UAS can provide an objective and data-rich 385 approach to describing plot height and offers a higher throughput approach to 386 quantification in lodging studies in the field. 387 We found that LiDAR and RGB analyses both produced similar results for stand height. 388 While LiDAR provided a higher correlation to manual measurements (Figure 3), the data 389 sizes were larger, analysis was more complex, and the equipment was more costly. 390 RGB images also provide additional data on plot color that could be analyzed with 391 additional calibration during photogrammetry for other research questions, such as plant 392 health, flowering time, and senescence. We recommend researchers consider the 393

of the experiment and the expected height differences of their control and 394 test lines when determining which sensor is appropriate to achieve the experimental 395 goals while keeping costs and analysis time low. For example, more subtle differences 396 between control and test varieties may require the higher accuracy afforded by LiDAR 397 to detect differences. 398 The height analysis from DEMs described above does not require elevation values to be 399 georectified to an absolute coordinate system, since height is determined by the 400 difference in elevation between plants and soil within a plot. This approach may be less 401 accurate when bare soil is not visible, such as when there is total canopy coverage, but 402 depending on the shape of plot height distributions, optimizing for a threshold may still 403 work. Residue (such as from no-till management), cover crops in biculture, and weeds 404 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 12 would also impact the appropriate approach to using UAS for plot heights. We also 405 found that the specific plot boundaries (i.e. how much soil area is included) can affect 406 the appropriate choice for soil and plant thresholds for the pixel distributions, which are 407 used to calculate average plot height. As such, we recommend visually inspecting the 408 shape of pixel height distributions (such as in Figure 2A) prior to running this analysis to 409 help choose appropriate threshold values, as was performed here (see Methods). Even 410 so, this method can produce variability due to stochasticity in plot boundaries, which 411 should be considered when interpreting results. 412 Importantly, the occurrence of the storm during this experiment was unintentional, and 413 obscured possible differences in lodging later in the season that can occur during grain 414 filling. It is possible that the effect of height is consistent across both lodging from wind 415 or rain and lodging from grain weight, but that cannot be determined in this study. 416 Instead, we focus on the ability of 3D point clouds to aid in both the tracking of height 417 changes through a season and on the prediction of possible yield loss from lodging. 418 Because of the correlation with both manual height measurements at a single time point 419 and the increased time resolution afforded, we conclude that UAS image analysis could 420 provide a benefit to tef breeding strategies that target resistance to lodging. 421 Figure legends 422 Figure 1 - UAS orthomosaics allowed for clear plot delimitation. (A) RGB orthomosaic 423 from July 17th, 2023. Note the powerlines (white lines) above the field. (B) DEM created 424 from the point cloud during stitching of the orthomosaic. The powerlines were converted 425 to no data values by PlantCV-Geospatial using a height threshold. (C) LiDAR from the 426 same date produced point clouds for height calculation. Higher points are toward the 427 red end of the scale. 428 429 Figure 2 - (A) Stand height was calculated as the difference between top (95%, blue 430 line) and bottom (1%, red line) percentiles from the point cloud distribution. (B) An 431 example of the 3D point cloud showing surrounding soil pixels. (C) A slice inside of a 432 plot in the point cloud showing the distribution of plant heights across the canopy. 433 434 Figure 3 - Measurements of height estimated from either RGB images (A) or LiDAR 435 data (B) correlated with manual measurements. RGB-estimated height was also 436 correlated with LiDAR-estimated height (C). 437 438 Figure 4 - Wild-type plants (orange) were taller than semidwarf lines (blue) early in the 439 season, but lodged more both after a storm event (red vertical line) and progressively 440 towards the end of the season. Colored line represents average of all wild-type or 441 semidwarf lines, respectively. 442 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 13 443 Figure 5 - Taller plants experienced a greater decrease in height after a storm (A) and 444 had a higher manual lodging score at the end of the season (B). There was variation in 445 height among semidwarf lines (circles) but the shorter lines were more resistant to 446 lodging. Color represents a replicated semidwarf line (n = 3). 447 448

449 UAV data were collected and provided by Remote Sensing Lab at Saint Louis University 450 as part of a Taylor Geospatial Institute Block Grant to the Donald Danforth Plant 451 Science Center. We acknowledge the use of Data to Science (D2S, https://d2s.org) 452 platform, an open-source project developed by Geospatial Data Science Lab 453 (https://gdsl.org) at Purdue University. We thank the Phenotyping Core Facility 454 (RRID:SCR_019049), particularly Joseph Duenwald for ground control point 455 maintenance, and the Field Research Site at the Donald Danforth Plant Science Center 456 for plant care. The developers of PlantCV-Geospatial thank Sam Taylor and Jalissa 457 Pirro for help and guidance. 458 459 Author Contributions 460 KMM, GB, and NF designed experiments. GB developed tef lines, planted, and 461 performed manual measurements of height and lodging score. KEB, HS, ML, and DS 462 performed data analysis. KEB, HS, and KMM wrote the article with contributions from all 463 authors. 464 Declarations of interests 465 Getu Beyene has patent “Lodging resistance in eragrostis tef” pending to Donald 466 Danforth Plant Science Center. 467 468 Funding 469 This work was supported by a Taylor Geospatial Institute Block Grant to K.M.M. and 470 N.F., the National Science Foundation (grant numbers 2120153 and 2346101 to N.F.), 471 the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether 472 Foundation. 473 474 Data Availability 475 Code and data associated with this manuscript are available on GitHub 476 (https://github.com/danforthcenter/teff-manuscript). 477 478 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 7, 2026. ; https://doi.org/10.64898/2026.01.06.697717doi: bioRxiv preprint 14 479 Supplemental Figure S1: Wind speed measured over a 48h period during which a 480 storm occurred in the vicinity of the field plots. 481 482

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