GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons.

By K. Nagel, A. Putz, F. Gilmer, Kathrin Heinz, Andreas Fischbach, Johannes Pfeifer, M. Faget, Stephan Blossfeld, M. Ernst, C. Dimaki and 6 more. 2012

331 citations recorded by the source corpus.

Subfields: Plant Science, Robotics & Control

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Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “…have been proposed to measure visual traits of plants in an automated fashion (Granier et al., 2006; Walter et al., 2007; Jansen et al., 2009; Nagel et al., 2012) together with customized image processing pipelines to analyze aspects of the acquired image data (Augustin et al., 2015; De…”
Breeding for increased nitrogen‐use efficiency: a review for wheat (T. aestivum L.) (2016, background): “At an intermediate scale, the use of soilfilled root observation chambers (rhizotrons or clear-pot) (e.g. Lobet et al. 2011, Nagel et al. 2012, Richard et al. 2015) and non-destructive digital imaging techniques offers some promises (Manschadi et al. 2006, 2010), as X-ray computed tomography…”
Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. (2020, unknown): “The throughput was 60 rhizotrons per hour out of a total capacity of 72 rhizotrons (Nagel et al., 2012).”
Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. (2012, background): “However, the plant biology community currently makes a great effort to develop automated systems for plant phenotyping (Granier et al. 2006; Nagel et al. 2012).”
Leaf segmentation in plant phenotyping: a collation study (2015, background): “While previously such phenotypes were annotated manually by experts, recently image-based nondestructive approaches are gaining attention among plant researchers to measure and study visual phenotypes of plants [24,28,41,55].”
Five decades of selection for yield reduced root length density and increased nitrogen uptake per unit root length in Australian wheat varieties (2016, background): “Visibility of the root system is not different in rhizo-boxes under the 30° and 45° angle when seeds are sown in contact with the acrylic glass side (Nagel et al. 2012; Pfeifer 2013).”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “While previously such phenotypes were annotated manually by experts, recently image-based nondestructive approaches are gaining attention among plant researchers to measure and study visual phenotypes of plants [24,28,41,55].”
Marker-Assisted Plant Breeding: Principles and Practices (2015, background): “In such a system, the proportion of roots observable at the transparent side of a rhizotron depends mainly on the plant species, and this proportion is loosely correlated with average root diameter (Nagel et al. 2012).”
Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “Additionally, automated systems have allowed researchers to reduce the labor needed to manage and perform large-scale growth screens in laboratory, greenhouse and field environments (Nagel et al. 2012).”
Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner] (2015, background): “In soil, roots can be imaged noninvasively using so-called rhizotrons [9], i.”
Plant phenotyping: from bean weighing to image analysis (2015, methodology): “These are the soil-filled 2D rhizotrons of the GROWSCREEN-Rhizo platform with a rooting depth of 90 cm [101] and 25 cm diameter-by-100 cm growth columns in combination with μ CT imaging at the Hounsfield facility of University of Nottingham [102].”
Root System Architecture and Its Association with Yield under Different Water Regimes in Durum Wheat (2018, background): “Several wheat studies have evaluated roots using different phenotyping methods including rhizotrons (Nagel et al., 2012; Lobet and Draye, 2013; Clarke et al., 2017), soil coring (Trachsel et al., 2011; Wasson et al., 2012; Wasson et al., 2014), lysimeters (Ehdaie et al., 2014; Elazab et al., 2016),…”
A Review of Imaging Techniques for Plant Phenotyping (2014, background): “Medicago truncatula [46]; Maize [47,48]; Bean [49] Controlled”