High-throughput non-destructive biomass determination during early plant development in maize under field conditions

By J. M. Montes, F. Technow, B. S. Dhillon, F. Mauch, A. Melchinger. 2011

149 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/bd719b93e80c2e7cbed786e748432a34a9bfa1d8

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Citations in the corpus

Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes (2012, result): “The number of PLSR factors of the combined model was higher than those of individual PLSR models but similar to that reported for prediction of maize biomass during early plant development (Montes et al., 2011).”
Plant phenotyping: from bean weighing to image analysis (2015, background): “Moreover, these setups are continuously being refined and their image-processing capabilities form the basis for a next generation of pheno-typing platforms that are operating in the field from different carriers such as tractors [18,19], blimps [20,21] or unmanned aerial vehicles [22].”
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.”
A Review of Imaging Techniques for Plant Phenotyping (2014, methodology): “In addition to the techniques reviewed, there are other technologies that have led to significant contributions to a plant phenotyping level of understanding about plant responses in heterogeneous environments, such as a microwave resonator prototype [12] and light curtain imaging (LCI) [78,200].”
Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley (2016, methodology): “The fusion of data from different spectral ranges or different physical sources can be done by multiple linear regressions, partial least square and other methods, such as support vector machines (Montes et al., 2011).”
REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Advanced phenotyping offers opportunities for improved breeding of forage and turf species (2012, methodology): “The advantage of such methods for improved breeding of the quality of forage and turf species is obvious; their application for this goal can hence be expected to be realized in the near future.”