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Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops
by
George-Jaeggli, Barbara
, Furbank, Robert T
, Potgieter, Andries B
, Deery, David M
, Jimenez-Berni, Jose A
in
Artificial intelligence
/ big data
/ Biomass
/ Breeding
/ canopy
/ canopy temperature
/ case studies
/ Cereal crops
/ computer vision
/ crop breeding
/ crop physiology
/ Crops
/ Crops, Agricultural - physiology
/ crossing
/ Edible Grain - physiology
/ Energy crops
/ Fluorescence
/ Gene sequencing
/ Genetic improvement
/ genome
/ Genomes
/ Genomics
/ Genotyping
/ grain crops
/ Green revolution
/ harvest index
/ Imaging techniques
/ Learning algorithms
/ leaves
/ Lidar
/ Machine learning
/ Machine vision
/ Phenomics
/ phenotype
/ Phenotyping
/ Photosynthesis
/ Physiology
/ Plant Breeding
/ Plant cover
/ Radiation
/ radiation use efficiency
/ Reflectance
/ Robotics
/ robots
/ sequence analysis
/ Sorghum
/ Spectral reflectance
/ stomatal conductance
/ Tansley review
/ Thermal imaging
/ thermography
/ Wheat
2019
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Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops
by
George-Jaeggli, Barbara
, Furbank, Robert T
, Potgieter, Andries B
, Deery, David M
, Jimenez-Berni, Jose A
in
Artificial intelligence
/ big data
/ Biomass
/ Breeding
/ canopy
/ canopy temperature
/ case studies
/ Cereal crops
/ computer vision
/ crop breeding
/ crop physiology
/ Crops
/ Crops, Agricultural - physiology
/ crossing
/ Edible Grain - physiology
/ Energy crops
/ Fluorescence
/ Gene sequencing
/ Genetic improvement
/ genome
/ Genomes
/ Genomics
/ Genotyping
/ grain crops
/ Green revolution
/ harvest index
/ Imaging techniques
/ Learning algorithms
/ leaves
/ Lidar
/ Machine learning
/ Machine vision
/ Phenomics
/ phenotype
/ Phenotyping
/ Photosynthesis
/ Physiology
/ Plant Breeding
/ Plant cover
/ Radiation
/ radiation use efficiency
/ Reflectance
/ Robotics
/ robots
/ sequence analysis
/ Sorghum
/ Spectral reflectance
/ stomatal conductance
/ Tansley review
/ Thermal imaging
/ thermography
/ Wheat
2019
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Do you wish to request the book?
Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops
by
George-Jaeggli, Barbara
, Furbank, Robert T
, Potgieter, Andries B
, Deery, David M
, Jimenez-Berni, Jose A
in
Artificial intelligence
/ big data
/ Biomass
/ Breeding
/ canopy
/ canopy temperature
/ case studies
/ Cereal crops
/ computer vision
/ crop breeding
/ crop physiology
/ Crops
/ Crops, Agricultural - physiology
/ crossing
/ Edible Grain - physiology
/ Energy crops
/ Fluorescence
/ Gene sequencing
/ Genetic improvement
/ genome
/ Genomes
/ Genomics
/ Genotyping
/ grain crops
/ Green revolution
/ harvest index
/ Imaging techniques
/ Learning algorithms
/ leaves
/ Lidar
/ Machine learning
/ Machine vision
/ Phenomics
/ phenotype
/ Phenotyping
/ Photosynthesis
/ Physiology
/ Plant Breeding
/ Plant cover
/ Radiation
/ radiation use efficiency
/ Reflectance
/ Robotics
/ robots
/ sequence analysis
/ Sorghum
/ Spectral reflectance
/ stomatal conductance
/ Tansley review
/ Thermal imaging
/ thermography
/ Wheat
2019
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Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops
Journal Article
Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops
2019
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Overview
Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high throughput techniques based on machine vision, robotics and computing (Plant Phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focusses on how field based plant phenomics can enable next generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from “Green Revolution” traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, chlorophyll fluorescence and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented. This article is protected by copyright. All rights reserved.
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