Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality

The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-des...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 15; H. 22; S. 5412
Hauptverfasser:	Lyu, Hongyi, Grafton, Miles, Ramilan, Thiagarajah, Irwin, Matthew, Wei, Hsiang-En, Sandoval, Eduardo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.11.2023
Schlagworte:	Aircraft algorithms Artificial neural networks Berries Brix value Cameras Correlation coefficient Correlation coefficients Electrical conductivity Electrical resistivity Environmental aspects Fruits Grapes Learning algorithms Machine learning Methods multispectral imagery Neural networks Non-destructive testing normalized difference vegetation index Normalized difference vegetative index prediction Prediction models Quality management Regression analysis Regression models Remote sensing Root-mean-square errors Satellites Sensors Soil conductivity soil electrical conductivity Soils sugar content Support vector machines total soluble solids UAV multispectral imagery Unmanned aerial vehicles Vegetation vegetation indices vigor vines Vineyards wine grape wine grapes Wineries & vineyards Wines New Zealand
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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