Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition

Accurately predicting grapevine yield and quality is critical for optimising vineyard management and ensuring economic viability. Numerous studies have reported the complexity in modelling grapevine yield and quality due to variability in the canopy structure, challenges in incorporating soil and mi...

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Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 16; no. 23; p. 4539
Main Authors:	Jewan, Shaikh, Gautam, Deepak, Sparkes, Debbie, Singh, Ajit, Billa, Lawal, Cogato, Alessia, Murchie, Erik, Pagay, Vinay
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.12.2024
Subjects:	Acidity Algorithms Bayesian analysis Berries Canopies canopy state variables Chlorophyll Clusters Composition Crop yields Data mining Decision trees Economics Fruits Gas exchange grapevine composition grapevine yield Grapevines Growing season International economic relations Learning algorithms Leaves Machine learning Mathematical models Modelling Parameters Photosynthesis Photosynthetically active radiation Physiology Predictions proximal sensing Regression analysis Regression models Sensors Soil structure State variable Stomata Stomatal conductance Vegetation Vegetation index vegetation indices Vineyards Water potential Weight Wine industry Wineries Wineries & vineyards Yield Australia United States
ISSN:	2072-4292, 2072-4292
Online Access:	Get full text
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