Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images

The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specificall...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 15; číslo 19; s. 4765
Hlavní autoři:	Gallo, Ignazio, Boschetti, Mirco, Rehman, Anwar Ur, Candiani, Gabriele
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.10.2023
Témata:	Agricultural production Algorithms Artificial intelligence Artificial neural networks calibration canopy Cereal crops Chlorophyll Comparative analysis Composition convolutional neural network Corn Crops Datasets deep learning Environmental aspects hybrid approach hyper-spectral images Hyperspectral imaging Image acquisition Learning algorithms Machine learning Measurement Multispectral photography Neural networks Nitrogen nitrogen content Nitrogen in the body Physiological aspects prediction Radiative transfer Remote sensing Satellites Self-supervised learning Sensors Simulation Testing Unmanned aerial vehicles Italy
ISSN:	2072-4292, 2072-4292
On-line přístup:	Získat plný text
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