A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes

Galli, Giovanni; Sabadin, Felipe; Costa‐Neto, Germano; Fritsche‐Neto, Roberto

doi:10.1007/s00122-020-03726-6

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…Finally, it is important to collect field calibration and validation data of phenotypic traits to be mapped. While in situ calibration data allow relationships and models to be developed, e.g., for UAV-based image classification, independent validation data enable the accuracy of maps to be assessed [ 163 ].…”

Section: Observations During Growthmentioning

confidence: 99%

Quinoa Phenotyping Methodologies: An International Consensus

Stanschewski¹,

Rey²,

Fiene³

et al. 2021

Plants

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Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

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Section: Observations During Growthmentioning

confidence: 99%

Quinoa Phenotyping Methodologies: An International Consensus

Stanschewski¹,

Rey²,

Fiene³

et al. 2021

Plants

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show abstract

“…A round positive buffer of 0.01 m was generated at the center of each plant to obtain the corresponding points of each plot. PH HTP was constructed from the difference between the 90 percentile of the top of the point cloud altitude and the pot altitude before plant germination (0 leaves) (Figure 2) (Galli et al., 2021). The image‐derived plant biomass index, f(biomass), was derived from the product of PH HTP (cm) and CC (cm 2 ) (Li et al., 2020) as f(biomass) = PH HTP

\times

CC.…”

Section: Methodsmentioning

confidence: 99%

A low‐cost greenhouse‐based high‐throughput phenotyping platform for genetic studies: A case study in maize under inoculation with plant growth‐promoting bacteria

Yassue

Galli

Borsato

et al. 2022

The Plant Phenome Journal

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Greenhouse‐based high‐throughput phenotyping (HTP) presents a useful approach for studying novel plant growth‐promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize (Zea Mays L.) cultivars exhibiting highly stable symbiosis with PGPB, greenhouse‐based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low‐cost greenhouse‐based HTP platform to collect growth‐related image‐derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen‐limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth in early developmental stages. Phenotypic correlations between the image‐derived phenotypes and manual measurements were at least 0.47 in the later stages of plant development. The genomic heritability estimates of the image‐derived phenotypes ranged from 0.23 to 0.54. Moderate‐to‐strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP‐based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low‐cost HTP platform for large‐scale genetic and management studies to capture plant growth.

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“…A round positive buffer of 0.01 m was generated at the center of each plant to obtain the corresponding points of each plot. PH HTP was constructed from the difference between the 90 percentile of the top of the point cloud altitude and the pot altitude before plant germination (0 leaves) (Figure 2) (Galli et al, 2021). The image-derived plant biomass index, f(biomass), was derived from the product of PH HTP (cm) and CC (cm 2 ) (Li et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: a case study in maize under inoculation with plant growth-promoting bacteria

Yassue

Galli

Borsato

et al. 2021

Preprint

Self Cite

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Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24-0.47) and between HTP-based plant height and manually measured plant height (0.55-0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth.

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A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes

Cited by 7 publications

References 36 publications

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa Phenotyping Methodologies: An International Consensus

A low‐cost greenhouse‐based high‐throughput phenotyping platform for genetic studies: A case study in maize under inoculation with plant growth‐promoting bacteria

A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: a case study in maize under inoculation with plant growth-promoting bacteria

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