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: Darra, N. (Author), Espejo-García, B. (Author), Fountas, S. (Author), Kasimati, A. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Antennas
Automated machine learning
AutoML
Bayesian optimization
correlation
Correlation
Correlation methods
Crop quality
Crops
ensemble methods
Ensemble methods
Forecasting
Global positioning system
Learning algorithms
Least squares approximations
NDVI
Nondestructive examination
Normalized difference vegetation index
Normalized difference vegetation index datum
Quality attributes
Quality characteristic
Quality control
quality prediction
Quality prediction
Regression analysis
Remote sensing
sugars
Sugars
Support vector machines
Unmanned aerial vehicles (UAV)
Vegetation
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