An automatic method based on daily in situ images and deep learning to date wheat heading stage-Reference-Cited by-同舟云学术

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An automatic method based on daily in situ images and deep learning to date wheat heading stage

Published:2020-07 Issue: Volume:252 Page:107793
ISSN:0378-4290
Container-title:Field Crops Research
language:en
Short-container-title:Field Crops Research

Author:

Velumani Kaaviya,Madec Simon,de Solan Benoit,Lopez-Lozano Raul,Gillet Jocelyn,Labrosse Jeremy,Jezequel Stephane,Comar Alexis,Baret Frédéric

Funder

Association Nationale de la Recherche et de la Technologie

Publisher

Elsevier BV

Subject

Soil Science,Agronomy and Crop Science

Reference61 articles.

1. Field high-throughput phenotyping: the new crop breeding frontier;Araus;Trends Plant Sci.,2014

2. Heat stress responses in a large set of winter wheat cultivars (Triticum aestivum L.) depend on the timing and duration of stress;Balla;PLoS One,2019

3. Leaf-rolling in maize crops: from leaf scoring to canopy-level measurements for phenotyping;Baret;J. Exp. Bot.,2018

4. Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model;Bogard;J. Exp. Bot.,2014

5. The development of the wheat spike;Bonnett;J. Agric. Res.,1936

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