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Front. Plant Sci. | doi: 10.3389/fpls.2019.00685

A High-Throughput Model-Assisted Method for Phenotyping Maize Green Leaf Area Index Dynamics Using Unmanned Aerial Vehicle Imagery

 Justin Blancon1, Dan Dutartre2, Marie-Hélène Tixier1, Marie Weiss3, Alexis Comar2,  Sébastien Praud1* and Frédéric Baret3
  • 1Biogemma (France), France
  • 2Hi-phen, France
  • 3INRA UMR Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, France

The dynamics of the Green Leaf Area Index (GLAI) is of great interest for numerous applications such as yield prediction and plant breeding. We present a high-throughput model-assisted method for characterizing GLAI dynamics in maize (Zea mays subsp. mays) using multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV). Two trials were conducted with a high diversity panel of 400 lines under well-watered and water deficient treatments in 2016 and 2017. For each UAV flight, we first derived GLAI estimates from empirical relationships between the multispectral reflectance and ground level measurements of GLAI achieved over a small sample of microplots. We then fitted a simple but physiologically sound GLAI dynamics model over the GLAI values estimated previously. Results show that GLAI dynamics was estimated accurately throughout the cycle (R²>0.9). Two parameters of the model, biggest leaf area and leaf longevity, were also estimated successfully. We showed that GLAI dynamics and the parameters of the fitted model are highly heritable (0.65≤H²≤0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modelling, UAV imagery and simple non destructive field measurements, provides new high-throughput tools for understanding the adaptation of GLAI dynamics and its interaction with the environment. GLAI dynamics is also a promising trait for crop breeding, and paves the way for future genetic studies.

Keywords: Green Leaf Area Index (GLAI), High-throughput phenotyping (HTP), unmanned aerial vehicle (UAV), maize (Zea mays subsp. mays), dynamics, drought, growth model, diversity panel

Received: 10 Jan 2019; Accepted: 07 May 2019.

Edited by:

Andreas Hund, ETH Zürich, Switzerland

Reviewed by:

Antonio Costa De Oliveira, Universidade Federal de Pelotas, Brazil
Ignacio A. Ciampitti, Kansas State University, United States  

Copyright: © 2019 Blancon, Dutartre, Tixier, Weiss, Comar, Praud and Baret. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Sébastien Praud, Biogemma (France), Paris, France,