Phenotyping wheat under salt stress conditions using a 3D laser scanner

Maphosa, Lancelot; Thoday-Kennedy, Emily; Vakani, Jignesh; Phelan, Andrew J.; Badenhorst, Pieter; Slater, Anthony T.; Spangenberg, Germán; Kant, Surya

doi:10.1080/07929978.2016.1243405

Cited by 14 publications

(8 citation statements)

References 33 publications

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“…greenhouse, growth chamber) experiments provide greater environmental control, more growing cycles, and are easier environments in which to study root phenotypes, although plants are often constrained by pot dimensions and less natural environmental interactions. For controlled-environment phenotyping, different automated platforms have been described ( Li et al , 2014 ; Poiré et al , 2014 ; Humplík et al , 2015 ; Neilson et al , 2015 ; Pandey et al , 2017 ), such as the Scanalyzer 3D imaging system, in which plants are placed on a conveyor system and moved to sensors for automated image acquisition ( Neilson et al , 2015 ), PlantEye, a 3D laser scanner mounted on a movable gantry, where a sensor is moved to image the plants ( Kjaer and Ottosen, 2015 ; Vadez et al , 2015 ; Maphosa et al , 2017 ), Phenoscope ( Tisné et al , 2013 ), Phenosis ( Granier et al , 2006 ), and Weighing, Imaging and Watering Machines (WIWAM) ( Skirycz et al , 2011 ).…”

Section: Introductionmentioning

confidence: 99%

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

Banerjee

Joshi

Thoday-Kennedy

et al. 2020

Journal of Experimental Botany

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The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.

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Section: Introductionmentioning

confidence: 99%

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

Banerjee

Joshi

Thoday-Kennedy

et al. 2020

Journal of Experimental Botany

Self Cite

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“…PlantEye was previously mainly applied in agronomy studies for the phenotyping of crop plant individuals and it was verified that the results of DWCP estimates correlate very well with manual measurements (Maphosa et al 2017, Manavalan et al 2021). It is equipped with an active sensor that projects a near‐infrared (940 nm) laser line vertically onto the plant canopy and captures the reflection of the laser and the reflectance in red, green, blue and near‐infrared wavelength with an inbuilt camera (Kjaer and Ottosen 2015, Maphosa et al 2017). Each scan has a resolution of < 1 mm pixel –1 and the data points of each scan are merged to generate a 3D point cloud.…”

Section: Methodsmentioning

confidence: 80%

Tracking succession by means of 3D scans of plant communities in a glacier forefield to infer assembly processes

He,

Hanusch,

Saueressig

et al. 2023

Oikos

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In primary successions, assembling plant communities are key for ecosystem functioning and stability. Often, plant successions are described on a taxonomic, functional and/or phylogenetic level, where species' identities, traits or evolutionary histories are considered. In this study, we exploited community features characterizing whole plant assemblages to capture emerging properties only available at the community level. Next to features aggregating over multiple plant species, we used a customized multispectral 3D plant scanner extracting digital community features as proxies for the frequency distribution of traits, productivity, prevalence of plant competition, and plant strategies and functions. Using these community features, we provide a comprehensive description of plant successions along glacier forefield. Additionally, we used a community feature‐based framework assessing the covariance of community feature dissimilarities and environmental dissimilarity to explore the mechanisms underlying plant community assemblies. Our data indicate a shift from fast‐growing and acquisitive, to slow‐growing and conservative plant strategies; increased productivity; higher competition between plant species; and an increasing contribution to the biogeomorphic stability along the successional gradient. Our results further indicate that stochastic processes dominate in early succession, whereas communities are mainly shaped by deterministic processes including environmental filtering and species interactions at late succession. We conclude that assessments of plant community features, facilitated by the use of field‐ready 3D scanners, provide complementary information to trait‐based approaches on the species level with implications for ecological processes.

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“…The approach involves application of sensor or image-based tools, to non-destructively and non-evasively measure crop traits across time and space. The screening tools can be handheld (Tracy et al ., 2020), airborne (Bian et al ., 2019), ground-based vehicles (Maphosa et al ., 2017), thermal and hyperspectral imagery or shovelomics (Chen et al ., 2017). Key production traits that can be captured include phenology, early vigour, crop growth status, water content, biomass, yield potential and grain quality (colour and size).…”

Section: Production Improvement Approachesmentioning

confidence: 99%

Bambara groundnut production, grain composition and nutritional value: opportunities for improvements

2022

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Bambara groundnut (Vigna subterranea L. Verdc; BGN) is an important legume grown mainly by small scale subsistence farmers in sub-Saharan Africa, in parts of Thailand and Indonesia. It has a high concentration of seed carbohydrate (55-70 %), protein (17-25 %), fat (1.4-12 %) and dietary fibre (5.2-6.4 %). A range of biotic and abiotic stresses together with socio-economic constraints affect its productivity, yield and quality. The changing climate

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Phenotyping wheat under salt stress conditions using a 3D laser scanner

Cited by 14 publications

References 33 publications

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

Tracking succession by means of 3D scans of plant communities in a glacier forefield to infer assembly processes

Bambara groundnut production, grain composition and nutritional value: opportunities for improvements

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