High-throughput plant phenotyping for improved turfgrass breeding applications

Vines, Phillip L.; Zhang, Jing

doi:10.48130/gr-2022-0001

Cited by 8 publications

(3 citation statements)

References 155 publications

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“…Canopy reflectance measurements should be incorporated into breeding programs to aid golf course superintendents in selecting cultivars based on potential biomass production and salinity tolerance. Vines and Zhang (2022) suggested that canopy reflectance would allow turfgrass breeders to assess larger numbers of turfgrass genotypes to efficiently identify elite germplasm to cultivar development to meet the future demands of the turfgrass industry. Three derivations of NDVI and a red edge position vegetation index are positively linearly correlated with Kentucky bluegrass ( Poa pratensis L.) biomass production (Poss et al., 2010).…”

Section: Resultsmentioning

confidence: 99%

A review of precision management for golf course turfgrass

Carlson¹,

Gaussoin

Puntel

2022

Crop Forage & Turfgrass Mgmt

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Precision turfgrass management (PTM) is a combination of methods and technologies proposed to increase the resiliency of golf courses by improving input efficiency while maintaining the function and aesthetics of the playing surface. However, there is no recent review describing the status of precision management in turfgrass. The objectives of this review were to (a) summarize peer reviewed research on precision technology for turfgrass management, (b) describe adoption of PTM-based tools, and (c) propose an agenda of research priorities to advance and promote PTM adoption. Of the articles reviewed, 94% documented the accuracy of sensors to detect turfgrass performance and stressors before or during visual symptoms. Only 6% of the research reviewed focused on developing models or decision support systems to quantify the relationship among reflectance, nitrogen uptake, visual quality, biomass production, and irrigation which are required for precision management by golf course superintendents. Efficacy or value of using PTM methods and technologies have not been reported. Golf course superintendents lack of knowledge about PTM, and lack of quantification of benefits of PTM pose limitations to promote adoption. Increasing the adoption of PTM will require research to focus additionally on automating sensor data processing; quantifying costs, benefits, and value of adopting PTM; and simplifying input applications in a PTM system. This review described the status of precision management in golf course turfgrass and shed light into the need for research to develop models and decision support tools for precision management of golf course turfgrass.

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Section: Resultsmentioning

confidence: 99%

A review of precision management for golf course turfgrass

Carlson¹,

Gaussoin

Puntel

2022

Crop Forage & Turfgrass Mgmt

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“…Image-based automated HTP integrates advanced software that is feasible to access for plant biology research [ 292 ]. Visible light imaging techniques are based upon a two-dimensional (2D) digital imaging system to measure leaf morphology, canopy coverage, above-ground dry matter, seed and panicle morphology, the architecture of root, shoot tip extension and yield-associated traits [ 293 , 294 ]. Apart from the 2D imaging system, 3D imaging techniques have also been reported to measure different characteristics in plants, such as leaf morphology, plant height, above-ground dry matter, crop structure and stature [ 295 ].…”

Section: Phenomicsmentioning

confidence: 99%

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses

Roychowdhury

Das²,

Gupta

et al. 2023

Genes

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The present day’s ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant’s innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the ‘omics’ toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant’s genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence ‘multi-omics’) integrated-omics approaches can decipher the plant’s abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop’s variable abiotic stress tolerance to ensure food security under changing environmental circumstances.

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“…To collect images, the lightbox is usually attached to a hand truck and wheeled from plot to plot. As summarized in their review of high-throughput phenotyping methods for turfgrass, this method has been used since the early 2000s in many studies for a variety of traits including rate of establishment, drought stress, and traffic tolerance (Vines & Zhang, 2022). Like many ground-based methods, this system provides reliable and reproducible data, but the time and labor required to move the lightbox to every plot is significant and often limits the frequency of measurements collected during times of drought stress.…”

Section: Introductionmentioning

confidence: 99%

Assessing unmanned aerial vehicle‐based imagery for breeding applications in St. Augustinegrass under drought and non‐drought conditions

Rockstad,

Austin,

Gouveia

et al. 2023

Crop Science

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The use of imagery collected from small unmanned aerial vehicles (UAVs) in turfgrass breeding has rapidly increased, as has the demand to develop drought‐resistant cultivars. However, prior to adopting UAVs to help guide turfgrass selection under drought stress conditions, a clear understanding of the value and predictive ability of imagery‐based turfgrass characterization is required. In St. Augustinegrass, a major warm‐season turfgrass species grown in the southeastern United States, limited research has been published about characterizing drought stress using aerial imagery. Specifically, no efforts have compared the various vegetation indices (VIs) commonly used to evaluate vegetative health in other species and sought to identify the most useful index for phenotyping drought stress traits in St. Augustinegrass. In this study, traditional ground‐based approaches for measuring percent green cover (PGC) and normalized difference vegetation index (NDVI) were compared against their UAV‐derived counterparts as well as thirteen VIs under drought and non‐drought conditions, and broad‐sense heritability (H2) was calculated. A population of 115 genotypes from a ‘Raleigh’ × ‘Seville’ cross were analyzed at two environmentally distinct field sites in North Carolina. At both sites, a significant relationship between ground‐based and UAV‐derived measurements for PGC and NDVI was observed before and during drought (r = 0.82 to 0.95) and suggests a clear advantage to using UAVs for phenotyping drought traits given the reduced time and labor costs compared to on‐ground efforts. Among all VIs compared, UAV‐derived NDVI showed strong correlation with the PGC taken on the ground (r > 0.85), a similar trend over time, and a higher H2 estimate under drought conditions, suggesting that UAV‐derived NDVI has the potential to assist in the selection of St. Augustinegrass genotypes with the best phenotypic response to drought. Implementing UAV imagery‐based high‐throughput methods will allow breeders to evaluate germplasm with unbiased quantitative consistency, quickly and thoroughly, and with increased frequency—all without sacrificing the response to selection potential.This article is protected by copyright. All rights reserved

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High-throughput plant phenotyping for improved turfgrass breeding applications

Cited by 8 publications

References 155 publications

A review of precision management for golf course turfgrass

A review of precision management for golf course turfgrass

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses

Assessing unmanned aerial vehicle‐based imagery for breeding applications in St. Augustinegrass under drought and non‐drought conditions

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