Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing.

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 limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multi

SUBMITTER: Carlier A 

PROVIDER: S-EPMC10694231 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature