Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data-Reference-Cited by-同舟云学术

Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data

Published:2020-01-10 Issue:3 Volume:102 Page:615-630
ISSN:0960-7412
Container-title:The Plant Journal
language:en
Short-container-title:Plant J

Author:

Vergara‐Diaz Omar¹^ORCID,Vatter Thomas¹,Kefauver Shawn Carlisle¹,Obata Toshihiro²,Fernie Alisdair R.²^ORCID,Araus José Luis¹

Affiliation:

1. Integrative Crop Ecophysiology Group Plant Physiology Section Faculty of Biology University of Barcelona Diagonal 643 08028 Barcelona Spain

2. Max Planck Institute of Molecular Plant Physiology Am Mühlenberg 1 14476 Potsdam Germany

Funder

Ministerio de Economía y Competitividad

Universitat de Barcelona

Generalitat de Catalunya

Publisher

Wiley

Subject

Cell Biology,Plant Science,Genetics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tpj.14636

Reference69 articles.

1. Plant Responses to Salt Stress: Adaptive Mechanisms

2. Field high-throughput phenotyping: the new crop breeding frontier

3. Atzberger C. Jarmer T. Schlerf M. Kotz B.andWerner W.(2003)Retrieval of wheat bio‐physical attributes from hyperspectral data and SAILH+ PROSPECT radiative transfer model.3rd EARSeL Work.Imaging Spectrosc 473–482. Available at:https://www.geo.fuberlin.de/geog/fachrichtungen/geoinformatik/medien/download/tjarmer/atzberger_jarmer_schlerf_koetz_werner_2003.pdf[Accessed October 3 2019]

4. Quantitative Analysis of Total Amino Acid in Barley Leaves under Herbicide Stress Using Spectroscopic Technology and Chemometrics

5. The infrared absorption of amino acid side chains

Cited by 37 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains;Food Chemistry;2024-12

2. Metabolomics as a Tool for Analysis of Wheat Leaves from Different Cultivars Infected with Pyricularia oryzae;ACS Agricultural Science & Technology;2024-04-24

3. Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data;Computers and Electronics in Agriculture;2024-01

4. Artificial intelligence/machine learning-assisted near-infrared/optical biosensing for plant phenotyping;Machine Learning and Artificial Intelligence in Chemical and Biological Sensing;2024

5. Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel;2023-12-16