Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries
Remote sensing technology, such as hyperspectral imaging, in combination with machine learning algorithms, has emerged as a viable tool for rapid and nondestructive assessment of wine grape ripeness. However, the differences in terroir, together with the climatic variations and the variability exhib...
Main Authors: | Véronique Gomes, Ana Mendes-Ferreira, Pedro Melo-Pinto |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/10/3459 |
Similar Items
-
Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries
by: Rui Silva, et al.
Published: (2018-02-01) -
Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging
by: Véronique Gomes, et al.
Published: (2021-07-01) -
Microbiota of different wine grape berries
by: Miroslava Kačániová, et al.
Published: (2019-03-01) -
Influence of Grape Berry Maturity on Juice and Base Wine Composition and Foaming Properties of Sparkling Wines from the Champagne Region
by: Pin-He Liu, et al.
Published: (2018-06-01) -
Performing sequential harvests based on berry sugar accumulation (mg/berry) to obtain specific wine sensory profiles
by: Guillaume Antalick, et al.
Published: (2021-04-01)