Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing
Alexis Carlier,
Sébastien Dandrifosse,
Benjamin Dumont
et al.
Abstract:Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limit… Show more
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