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

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          Abstract

          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 (R 2 > 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 ≤ H 2 ≤ 0.98), responsive to environmental conditions, and linked to yield and drought tolerance. This method, combining growth modeling, 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.

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              An overview of APSIM, a model designed for farming systems simulation

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                06 June 2019
                2019
                : 10
                Affiliations
                1Biogemma, Centre de Recherche de Chappes , Chappes, France
                2HIPHEN SAS , Avignon, France
                3INRA UMR 114 EMMAH, UMT CAPTE, Domaine Saint-Paul , Avignon, France
                Author notes

                Edited by: Andreas Hund, ETH Zürich, Switzerland

                Reviewed by: Ignacio Antonio Ciampitti, Kansas State University, United States; Antonio Costa De Oliveira, Universidade Federal de Pelotas, Brazil

                *Correspondence: Sébastien Praud sebastien.praud@ 123456biogemma.com

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2019.00685
                6568052
                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.

                Page count
                Figures: 6, Tables: 4, Equations: 11, References: 111, Pages: 16, Words: 13225
                Funding
                Funded by: Association Nationale de la Recherche et de la Technologie 10.13039/501100003032
                Award ID: 2015/1190
                Categories
                Plant Science
                Original Research

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