Calibrated plant height estimates with structure from motion from fixed-wing UAV images

Han, Xiongzhe; Thomasson, J. Alex; Bagnall, Cody; Pugh, N. Ace; Horne, David W.; Rooney, William L.; Malambo, Lonesome; Chang, Anjin; Jung, Jinha; Cope, Dale

doi:10.1117/12.2305746

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…This is a minimum cost of $3,280 per season for just collection of plant height information. Although DSM and other methods (e.g., LiDAR, proximal imagery) have been previously used to measure plant and canopy heights, they were used for crops like sorghum, wheat, and corn with vertical growth habit (Han et al, 2018;Madec et al, 2017;Watanabe et al, 2017;Wang et al, 2018;Yuan F I G U R E 7 Relationship between manually taken plant height and derived height from regression equation (Equations 1 and 2) for (a) the ground point (GP) method, and (b) the digital terrain model (DTM) method. The data points represented actual height of peanut canopy in centimeters.…”

Section: Discussionmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAV) photogrammetry was successfully used to determine plant height in sorghum [Sorghum bicolor (L) Moench], wheat (Triticum aestivum L.), barley (Hordeum vulgare L), and corn (Bendig, Bolten, & Bareth, 2013;Freeman et al, 2007;Holman et al, 2016;Watanabe et al, 2017;Demir, Sönmez, Akar, & Ünal, 2018;Han et al, 2018;Wang, Singh, Marla, Morris, & Poland, 2018;Yuan et al, 2018). In these studies, the authors compared manually measured plant height with height derived T A B L E 1 Weekly dates for unmanned aerial vehicle (UAV) flight with red-green-blue (RGB) camera and manual height measurement of peanut plots for the mini-core study, 2017 along with weeks after planting (WAP) using 3D surface models created using structure from motion (SfM) photogrammetry (Bendig et al, 2013;Holman et al, 2016;Demir et al, 2018).…”

Section: Core Ideasmentioning

confidence: 99%

“…In these studies, the authors compared manually measured plant height with height derived T A B L E 1 Weekly dates for unmanned aerial vehicle (UAV) flight with red-green-blue (RGB) camera and manual height measurement of peanut plots for the mini-core study, 2017 along with weeks after planting (WAP) using 3D surface models created using structure from motion (SfM) photogrammetry (Bendig et al, 2013;Holman et al, 2016;Demir et al, 2018). For example, in sorghum and corn, Watanabe et al (2017), Han et al (2018), and Wang et al (2018) successfully estimated plant height using SfM photogrammetry and digital surface model (DSM) models. In contrast, Wang et al (2018) and Yuan et al (2018) used LiDAR mounted on an UAV for height estimation in sorghum and corn, and suggested that LiDAR is a reliable method for height estimation.…”

Section: Core Ideasmentioning

confidence: 99%

“…(2017), Han et al. (2018), and Wang et al. (2018) successfully estimated plant height using SfM photogrammetry and digital surface model (DSM) models.…”

Section: Informationmentioning

confidence: 99%

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High‐throughput measurement of peanut canopy height using digital surface models

Sarkar

Cazenave

Oakes

et al. 2020

The Plant Phenome Journal

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Peanut (Arachis hypogaea L.) is an important food and oilseed crop in the United States and worldwide with high net returns. However, input costs are high (US$1,970-$2,220 ha −1), and yield in excess of 4,500 kg ha −1 is needed to offset the costs. Since yield is limited by biotic and abiotic stresses, newer cultivars with tolerance to these stresses are needed to optimize yield. Plant height and canopy architecture may affect crop water use and plant disease resistance. However, measuring canopy height is a time-consuming process. Surface elevation models from red-green-blue (RGB) aerial images have been successfully developed to estimate genetic differences of plant height for tall crops like corn (Zea mays L.) and sorghum [Sorghum bicolor (L) Moench]; but they have not been tested for short crops like peanut with a runner growth habit and much smaller height differences among genotypes. The objective of this study was to derive canopy height of peanut from digital surface models (DSM). Images were aerially taken using a digital camera mounted on an unmanned aerial vehicle (UAV). Images were orthomosaiced to create the DSM and the digital terrain model (DTM) of the plot. Canopy height was derived by subtracting the DTM from the DSM in ArcGIS software. Results showed that the RGB derived canopy height was highly correlated to the manually measured height (R 2 = .953). We propose the methods used here as fast and relatively easy selection tools for breeders and crop growth evaluators of peanut plant height. 1 INFORMATION Peanut (Arachis hypogaea L.) is an important oil and food crop, grown on 17 million ha worldwide. In the United States, peanut is a high net return crop grown annually on Abbreviations: DSM, digital surface model; DTM, digital terrain model; GP, ground point; GSD, ground sample distance; RGB, red-green-blue; SfM, structure from motion; UAV, unmanned aerial vehicle; WAP, weeks after planting. 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.

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Section: Discussionmentioning

confidence: 99%

Section: Core Ideasmentioning

confidence: 99%

Section: Core Ideasmentioning

confidence: 99%

“…(2017), Han et al. (2018), and Wang et al. (2018) successfully estimated plant height using SfM photogrammetry and digital surface model (DSM) models.…”

Section: Informationmentioning

confidence: 99%

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High‐throughput measurement of peanut canopy height using digital surface models

Sarkar

Cazenave

Oakes

et al. 2020

The Plant Phenome Journal

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“…Deployment of high‐throughput technologies for field phenotyping has been limited in sorghum to date, but development and testing of instrumentation for collecting field data fast and accurately is ongoing with more efforts likely to follow. The latest technology for in‐field sorghum phenotyping currently consists of remote sensing (Shafian et al ., ), UAVs (Shi et al ., ; Han et al ., ), autonomous ground robots (Salas‐Fernandez et al ., ), advanced imaging (Potgieter et al ., ) and machine learning (Vijayarangan et al ., ). For sorghum, plant height (Salas‐Fernandez et al ., ; Watanabe et al ., ; Hu et al ., ; Pugh et al ., ) and stalk width (Salas‐Fernandez et al ., ; Gomez et al ., ) are the two major traits that have been successfully phenotyped by high‐throughput phenotyping platforms in the field.…”

Section: Advances To Identify and Utilize Genetic Variation For Sorghmentioning

confidence: 99%

Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments

2018

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Summary With the recent development of genomic resources and high‐throughput phenotyping platforms, the 21st century is primed for major breakthroughs in the discovery, understanding and utilization of plant genetic variation. Significant advances in agriculture remain at the forefront to increase crop production and quality to satisfy the global food demand in a changing climate all while reducing the environmental impacts of the world's food production. Sorghum, a resilient C4 grain and grass important for food and energy production, is being extensively dissected genetically and phenomically to help connect the relationship between genetic and phenotypic variation. Unlike genetically modified crops such as corn or soybean, sorghum improvement has relied heavily on public research; thus, many of the genetic resources serve a dual purpose for both academic and commercial pursuits. Genetic and genomic resources not only provide the foundation to identify and understand the genes underlying variation, but also serve as novel sources of genetic and phenotypic diversity in plant breeding programs. To better disseminate the collective information of this community, we discuss: (i) the genomic resources of sorghum that are at the disposal of the research community; (ii) the suite of sorghum traits as potential targets for increasing productivity in contrasting environments; and (iii) the prospective approaches and technologies that will help to dissect the genotype–phenotype relationship as well as those that will apply foundational knowledge for sorghum improvement.

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Role of the Genomics–Phenomics–Agronomy Paradigm in Plant Breeding

Chen

Rutkoski

Schnable

et al. 2022

Plant Breeding Reviews

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Highlights• Phenomics allows genetic mapping of traits throughout plant growth and a better understanding of both pleiotropy and endophenotypes. • Many GWAS identified loci are not validated and remain unused.• Genomics provides a path to validate phenomics traits, especially the ones without direct links to agronomics traits. • Multiple trait GBLUP remains the most sophisticated method to integrate agronomics and phenomics traits but does not demonstrate consistent advantage across traits and environments. • Bayesian methods have been extended to incorporate multiple traits, multiple environments, and prior biological knowledge. • Prediction using explainable machine learning incorporates domain knowledge with complex modules allowing to learn from data but guided by confirmed domain knowledge rather than less reliable human expert experiences.

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Calibrated plant height estimates with structure from motion from fixed-wing UAV images

Cited by 4 publications

References 7 publications

High‐throughput measurement of peanut canopy height using digital surface models

High‐throughput measurement of peanut canopy height using digital surface models

Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments

Role of the Genomics–Phenomics–Agronomy Paradigm in Plant Breeding

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