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.
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