Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model

Shouyang Liu 1 Frédéric Baret 1 Mariem Abichou 2 Frédéric Boudon 3, 4 Samuel Thomas 5 Kaiguang Zhao 6 Christian Fournier 7, 4 Bruno Andrieu 2 Kamran Irfan 1 Matthieu Hemmerlé 8 Benoit De Solan 5
1 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
2 ECOSYS - Ecologie fonctionnelle et écotoxicologie des agroécosystèmes
3 UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
4 VIRTUAL PLANTS - Modeling plant morphogenesis at different scales, from genes to phenotype
UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations, CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique
5 ARVALIS - Institut du Végétal [Boigneville]
6 Ohio Agricultural Research and Development Ohio Agricultural Research and Development Center, The Ohio State University
7 LEPSE - Écophysiologie des Plantes sous Stress environnementaux
8 Hiphen Integrated Plant Phenotyping Systems
Abstract : The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development stage when saturation effects are known to limit the performances of standard indirect methods based either on the gap fraction or reflectance measurements. The LiDAR provides both the three dimensional (3D) point cloud from which the vertical distribution (Z profile) of the interception points is computed, as well as the intensity of the returned signal from which the green fraction (GF) is derived. The data were interpreted by exploiting the 3D ADEL-Wheat model that synthesizes the knowledge accumulated on wheat canopy structure. A LiDAR simulator that accounts for the specific observation configuration used was developed to mimic the actual LiDAR measurements. The in-silico experiments were conducted to generate training and validation dataset. Neural network were then used to estimate GAI from the Z profile and GF derived from the LiDAR measurements. Performances of GAI estimates by the several methods investigated were evaluated using either experimental data with 3 < GAI < 6 and data simulated with the 3D structure model with 1 < GAI < 7. Results confirm that using only the GF provides poor estimates of GAI (0.89 < RMSE < 1.28; 0.22 < rRMSE < 0.31), regardless of turbid medium or realistic assumptions on canopy 3D structure. The introduction of the Z profile information improved significantly the GAI estimation accuracy (0.48 < RMSE < 0.55; 0.12 < rRMSE < 0.13). This study demonstrates the interest of using the third dimension provided by LiDAR to better estimate GAI in crops under high GAI values. However, this requires the use of a realistic 3D structure crop model over which the LiDAR data could be simulated under the observational configuration used.
Keywords : GAI ADEL-Wheat Wheat LiDAR Neural networks 3D canopy structure model Poisson model Phenotyping
Type de document :
Article dans une revue
Agricultural and Forest Meteorology, Elsevier Masson, 2017, 247, pp.12 - 20. 〈10.1016/j.agrformet.2017.07.007〉
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https://hal.inria.fr/hal-01573077
Contributeur : Christophe Godin <>
Soumis le : mardi 8 août 2017 - 14:52:11
Dernière modification le : vendredi 22 mars 2019 - 11:48:44

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INRA | TDS-MACS | AGROPOLIS | CIRAD | INRIA2017 | AGROPARISTECH | INRIA | UNIV-PARIS-SACLAY | ECOSYS | UNIV-MONTPELLIER | B3ESTE | AMAP | UNIV-AVIGNON

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Shouyang Liu, Frédéric Baret, Mariem Abichou, Frédéric Boudon, Samuel Thomas, et al.. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model. Agricultural and Forest Meteorology, Elsevier Masson, 2017, 247, pp.12 - 20. 〈10.1016/j.agrformet.2017.07.007〉. 〈hal-01573077〉

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