Field-based phenomics for plant genetics research

By Jeffrey W. White, Pedro Andrade-Sanchez, Michael A. Gore, Kevin F. Bronson, T. Coffelt, M. Conley, K. A. Feldmann, A. French, J. Heun, D. Hunsaker and 7 more. 2012

571 citations recorded by the source corpus.

Subfields: Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/0ba3dbac67f1936d87ff771d67b6ebe1370fd0e4

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Citations in the corpus

Root Phenotyping for Drought Tolerance: A Review (2018, background): “Variability also occurs due to differences in canopy densities of different genotypes [157].”
Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle (2016, background): “On the other hand, field-based challenges for the collection of phenotyping measurements have recently been reported (Chapman et al., 2014; Fiorani & Schurr, 2013; White et al., 2012).”
Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. (2015, unknown): “[6], Fabio et al.”
Marker-Assisted Plant Breeding: Principles and Practices (2015, background): “In order to be able to estimate genotype environment interactions, a set of environmental factors should be recorded during the experimental period (White et al. 2012; Araus and Cairns 2014).”
Plant phenotyping: from bean weighing to image analysis (2015, background): “Therefore, thermal imaging offers a large potential for non-destructive measurement of plant water status for irrigation management and for phenotyping [62] in the context of stress tolerance or drought stress avoidance [63].”
Model-assisted phenotyping and ideotype design (2015, background): “in terms of phenotyping, field and controlledconditions platforms (Pieruschka, and Poorter, 2012; White et al., 2012; Fiorani and Schurr, 2013) and networks of field experiments (Hammer et al.”
Image-based plant phenotyping with incremental learning and active contours (2014, background): “Plant phenotyping can occur on small scale (controlled laboratory settings of a growth chamber), in the green house, or in the field (Finkel, 2009; Walter et al., 2012; White et al., 2012).”
Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley (2016, methodology): “A number f workgroups have used carrier vehicles equipped with spectral nd other sensors to increase the throughput of trait assessments n breeding nurseries and field trials (Schmidhalter et al., 2001; ontes et al., 2011; White et al., 2012; Comar et al., 2012; Rebetzke t al., 2013).”
Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “Recently, it has been argued that automated, highthroughput, field-based organismal phenotyping techniques involving remote sensing (such as near-infrared spectroscopy mounted on agricultural harvesters to measure spectral canopy reflectance with the aid of global positioning system (GPS)-guided tra”
Plant adaptation to climate change—opportunities and priorities in breeding (2012, background): “These measures can be applied in glasshouses (Sirault et al. 2009; Montes et al. 2011; White et al. 2012) or aerial vehicles (Merz and Chapman 2006; Lelong et al.”
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping (2014, background): “Obtaining useful information from proximal remote sensing buggies for use by breeders and physiologists is a considerable challenge that has been identified by others [11].”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, background): “For example, more than 40 h were required to cover the 20,000 plots with a single vehicle traveling at 2 km per h to measure traits on single rows (White et al., 2012).”