Plant height (PH) data collected at high temporal resolutions can give insight into how genotype and environmental variation influence plant growth. However, in order to increase the temporal resolution of PH data collection, more robust, rapid, and low‐cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) provide an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities. PH data was collected using both weekly UAV flights and manual measurements. The comparisons of UAV‐based and manually acquired PH measurements revealed sources of error in measuring PH and were used to develop a robust pipeline for generating UAV‐based PH estimates. This pipeline was utilized to document differences in the rate of growth between genotypes and planting dates. Our results also demonstrate that growth rates generated by PH measurements collected at multiple timepoints early in development can be useful in improving predictions of PH at the end of the season. This method provides a low cost, high throughput method for evaluating plant growth in response to environmental stimuli on a plot basis that can be implemented at the scale of a breeding program.

1 2 Both stalk and root lodging can cause significant yield losses in maize; however, maize plants 3 are often able to recover from root lodging. There is potential among breeding programs for 4 developing lines that are more tolerant and can more rapidly recover from root lodging. We 5 assessed the incidence of root lodging utilizing end-of-season lodging scores collected among 6 the Genomes 2 Fields (G2F) initiative trials and found a large yet variable incidence of lodging 7 across states, years, and genotypes. Lodging in this dataset was scored manually at the end of 8 the season, and little is known about the drivers of lodging and lodging recovery. We therefore 9 developed an approach for utilizing temporal plant height measurements collected from 10 unmanned aerial vehicles to capture in-season lodging and recovery in a yield trial consisting of 11 24 maize hybrids planted in replicate under two dates and three planting densities in St Paul, 12 MN in the summers of 2018 and 2019. We found that growth rates during vegetative 13 development as well as the developmental timing of plants when exposed to a storm are 14 predictive of the amount of lodging maize plots will experience. We also found that utilizing 15 temporal height measurements can help in not just estimating lodging and early vegetative 16 growth rates, but that utilizing these estimates can also aid in assessing end of season yield. 17 18 ABBREVIATIONS 19 20 DEM -Digital Elevation Model 21 G2F -Genome 2 Fields 22 PH -Plant Height 23 UAV -Unmanned Aerial Vehicle 24

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Developing the resilient crops of the future will require access to a broad set of tools. While advances in sequencing and marker technologies have facilitated marker‐trait associations and the ability to predict the phenotype of an individual from its genotypic information, other tools such as high‐throughput phenotyping are still in their infancy. Advances in sensors, aeronautics, and computing have enabled progress. Here, we review current platforms and sensors available for top‐down field phenotyping with a focus on unoccupied aerial vehicles (UAVs) and red, green, blue sensors. We also review the ability and effectiveness of extracting traits from images captured using combinations of these platforms and sensors. Improvements in trait standardization and extraction software are expected to increase the use of high‐throughput phenotyping in the coming years and further facilitate crop improvement.

Plant height (PH) data collected at high temporal resolutions can give insight into important growth parameters useful for identifying elite material in plant breeding programs and developing management guidelines in production settings. However, in order to increase the temporal resolution of PH data collection, more robust, rapid and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) can be an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities in St Paul, MN in the summer of 2018. The field was imaged weekly after planting using a DJI Phantom 4 Advanced drone to extract PH and hand measurements were collected following aerial imaging of the field. In this work, we test the error in UAV PH measurements and compare it to the error obtained within manually acquired PH measurements. We also propose a method for improving the correspondence of manual and UAV measured height and evaluate the utility of using UAV obtained PH data for assessing growth of maize genotypes and for estimating end-season height.

Author contributions: SBT, TAE and NMS conceived the experiments; SBT, TAE and SSD conducted the experiments, SBT and TAE conducted the analyses; SBT and NMS wrote the manuscript Keywords: hyperspectral imaging, plant phenotyping, abiotic stress, maize Conflict of interest: The authors do not have any conflict of interest to declare. Abstract 1There is significant enthusiasm about the potential for hyperspectral imaging to document 2 variation among plant species, genotypes or growing conditions. However, in many cases the 3 application of hyperspectral imaging is performed in highly controlled situations that focus on a 4 flat portion of a leaf or side-views of plants that would be difficult to obtain in field settings. We 5 were interested in assessing the potential for applying hyperspectral imaging to document 6 variation in genotypes or abiotic stresses in a fashion that could be implemented in field settings. 7Specifically, we focused on collecting top-down hyperspectral images of maize seedlings similar 8 to a view that would be collected in a typical maize field. A top-down image of a maize seedling 9 includes a view into the funnel-like whorl at the center of the plant with several leaves radiating 10 outwards. There is substantial variability in the reflectance profile of different portions of this 11 plant. To deal with the variability in reflectance profiles that arises from this morphology we 12 implemented a method that divides the longest leaf into 10 segments from the center to the leaf 13 tip. We show that using these segments provides improved ability to discriminate different 14 genotypes or abiotic stress conditions (heat, cold or salinity stress) for maize seedlings. We also 15 found substantial differences in the ability to successfully classify abiotic stress conditions 16 among different inbred genotypes of maize. This provides an approach that can be implemented 17 to help classify genotype and environmental variation for maize seedlings that could be 18 implemented in field settings. 19 Significance Statement 20This study describes the importance of using spatial information for the analysis of hyperspectral 21 images of maize seedling. The segmentation of maize seedling leaves provides improved containing plant tissue and thresholded to generate a binary image mask to extract the reflectance 129 values at each wavelength for entire plants. 130This approach was applied to several different experiments that are summarized in Table S1. We 131 sought to address different themes in our analyses of this data. First, different genotypes or 132 environments often result in changes in plant morphology (Enders et al., 2019). We evaluated the 133 potential of hyperspectral data to capture morphological differences by utilizing changes in

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There is significant enthusiasm about the potential for hyperspectral imaging to document variation among plant species, genotypes, or growing conditions. However, in many cases the application of hyperspectral imaging is performed in highly controlled situations that focus on a flat portion of a leaf or side-views of plants that would be difficult to obtain in field settings. We were interested in assessing the potential for applying hyperspectral imaging from a top-down view to document variation in genotypes and abiotic stresses for maize (Zea mays L.) seedlings grown in controlled environments. A top-down image of a maize seedling includes a view into the funnel-like whorl at the center of the plant with several leaves radiating outward. There is substantial variability in the reflectance profile of different portions of this plant. To deal with the variability in reflectance profiles that arises from this morphology we implemented a method that divides the longest leaf into 10 segments of equal length from the center to the leaf tip. We show that there is large variability in the hyperspectral profiles across leaf segments, which are masked when performing whole-plant averages as tend to be done when analyzing hyperspectral data. We found that using these segments provides improved ability to discriminate different genotypes (B73, Mo17, Ki11, MS71, PH207) and abiotic stress conditions (heat, cold, or salinity stress) for maize seedlings. This provides an approach that can be implemented to help classify genotype and environmental variation for maize seedlings from a top-down view such as that which would be collected in field settings. 1 INTRODUCTION Abiotic stresses cause major yield declines across many crops and can limit production by up to 70% (Boyer, 1982; Majeed & Muhammad, 2019). Advances in molecular tools have greatly facilitated breeders in efficiently identifying and Abbreviations: DAS, days after sowing; PCA, principal component analysis; RGB, red-green-blue; SVM, support vector machine; ZT, Zeitgeber Time. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.