Imaging Wheat Canopy Through Stereo Vision: Overcoming the Challenges of the Laboratory to Field Transition for Morphological Features Extraction

Dandrifosse, Sébastien; Bouvry, Arnaud; Leemans, Vincent; Dumont, Benjamin; Mercatoris, Benoît

doi:10.3389/fpls.2020.00096

Article (Scientific journals)

Imaging Wheat Canopy Through Stereo Vision: Overcoming the Challenges of the Laboratory to Field Transition for Morphological Features Extraction

Dandrifosse, Sébastien; Bouvry, Arnaud; Leemans, Vincent et al.

2020 • In Frontiers in Plant Science

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https://hdl.handle.net/2268/250348

DOI
10.3389/fpls.2020.00096

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Keywords :

wheat; stereo vision; crop phenotyping; leaf area index; canopy height; mean tilt angle; leaf angle distribution

Abstract :

[en] Stereo vision is a 3D imaging method that allows quick measurement of plant architecture. Historically, the method has mainly been developed in controlled conditions. This study identified several challenges to adapt the method to natural field conditions and propose solutions. The plant traits studied were leaf area, mean leaf angle, leaf angle distribution, and canopy height. The experiment took place in a winter wheat, Triticum aestivum L., field dedicated to fertilization trials at Gembloux (Belgium). Images were acquired thanks to two nadir cameras. A machine learning algorithm using RGB and HSV color spaces is proposed to perform soil-plant segmentation robust to light conditions. The matching between images of the two cameras and the leaf area computation was improved if the number of pixels in the image of a scene was binned from 2560 × 2048 to 1280 × 1024 pixels, for a distance of 1 m between the cameras and the canopy. Height descriptors such as median or 95th percentile of plant heights were useful to precisely compare the development of different canopies. Mean spike top height was measured with an accuracy of 97.1 %. The measurement of leaf area was affected by overlaps between leaves so that a calibration curve was necessary. The leaf area estimation presented a root mean square error (RMSE) of 0.37. The impact of wind on the variability of leaf area measurement was inferior to 3% except at the stem elongation stage. Mean leaf angles ranging from 53° to 62° were computed for the whole growing season. For each acquisition date during the vegetative stages, the variability of mean angle measurement was inferior to 1.5% which underpins that the method is precise.

Disciplines :

Agriculture & agronomy

Author, co-author :

Dandrifosse, Sébastien ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges

Bouvry, Arnaud ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges

Leemans, Vincent ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges

Dumont, Benjamin ; Université de Liège - ULiège > Département GxABT > Plant Sciences

Mercatoris, Benoît ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges

Language :

English

Title :

Imaging Wheat Canopy Through Stereo Vision: Overcoming the Challenges of the Laboratory to Field Transition for Morphological Features Extraction

Publication date :

18 February 2020

Journal title :

Frontiers in Plant Science

eISSN :

1664-462X

Publisher :

Frontiers Media S.A., Lausanne, Switzerland

Peer reviewed :

Peer Reviewed verified by ORBi

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
DGRNE - Région wallonne. Direction générale des Ressources naturelles et de l'Environnement [BE]

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