Estimates of maize plant density from UAV RGB images using Faster-RCNN
detection model: impact of the spatial resolution
- URL: http://arxiv.org/abs/2105.11857v1
- Date: Tue, 25 May 2021 11:54:51 GMT
- Title: Estimates of maize plant density from UAV RGB images using Faster-RCNN
detection model: impact of the spatial resolution
- Authors: Kaaviya Velumani, Raul Lopez-Lozano, Simon Madec, Wei Guo, Joss
Gillet, Alexis Comar, Frederic Baret
- Abstract summary: High-resolution (HR) images are required to detect small plants present at early stages.
This study explores the impact of image ground sampling distance (GSD) on the performances of maize plant detection at 3-5 leaves stage using Faster-RCNN.
- Score: 2.757597890229683
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Early-stage plant density is an essential trait that determines the fate of a
genotype under given environmental conditions and management practices. The use
of RGB images taken from UAVs may replace traditional visual counting in fields
with improved throughput, accuracy and access to plant localization. However,
high-resolution (HR) images are required to detect small plants present at
early stages. This study explores the impact of image ground sampling distance
(GSD) on the performances of maize plant detection at 3-5 leaves stage using
Faster-RCNN. Data collected at HR (GSD=0.3cm) over 6 contrasted sites were used
for model training. Two additional sites with images acquired both at high and
low (GSD=0.6cm) resolution were used for model evaluation. Results show that
Faster-RCNN achieved very good plant detection and counting (rRMSE=0.08)
performances when native HR images are used both for training and validation.
Similarly, good performances were observed (rRMSE=0.11) when the model is
trained over synthetic low-resolution (LR) images obtained by down-sampling the
native training HR images, and applied to the synthetic LR validation images.
Conversely, poor performances are obtained when the model is trained on a given
spatial resolution and applied to another spatial resolution. Training on a mix
of HR and LR images allows to get very good performances on the native HR
(rRMSE=0.06) and synthetic LR (rRMSE=0.10) images. However, very low
performances are still observed over the native LR images (rRMSE=0.48), mainly
due to the poor quality of the native LR images. Finally, an advanced
super-resolution method based on GAN (generative adversarial network) that
introduces additional textural information derived from the native HR images
was applied to the native LR validation images. Results show some significant
improvement (rRMSE=0.22) compared to bicubic up-sampling approach.
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