Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning

Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning

Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Anal...

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Bibliographic Details
Main Authors: Aikaterini Kasimati, Borja Espejo-García, Nicoleta Darra, Spyros Fountas
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Sensors
Subjects:
NDVI
AutoML
Bayesian optimization
ensemble methods
correlation
quality prediction
Online Access:https://www.mdpi.com/1424-8220/22/9/3249

Internet

https://www.mdpi.com/1424-8220/22/9/3249

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