Towards an Improved High-Throughput Phenotyping Approach: Utilizing MLRA and Dimensionality Reduction Techniques for Transferring Hyperspectral Proximal-Based Model to Airborne Images

Towards an Improved High-Throughput Phenotyping Approach: Utilizing MLRA and Dimensionality Reduction Techniques for Transferring Hyperspectral Proximal-Based Model to Airborne Images

At present, it is critical to accurately monitor wheat crops to help decision-making processes in precision agriculture. This research aims to retrieve various wheat crop traits from hyperspectral data using machine learning regression algorithms (MLRAs) and dimensionality reduction (DR) techniques....

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Bibliographic Details
Main Authors: Ramin Heidarian Dehkordi, Gabriele Candiani, Francesco Nutini, Federico Carotenuto, Beniamino Gioli, Carla Cesaraccio, Mirco Boschetti
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Remote Sensing
Subjects:
high-throughput phenotyping
proximal sensing
machine learning
dimensionality reduction
transferability
Online Access:https://www.mdpi.com/2072-4292/16/3/492

Internet

https://www.mdpi.com/2072-4292/16/3/492

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