Incorporation of visible/near-infrared spectroscopy and machine learning models for indirect assessment of grape ripening indicators

Incorporation of visible/near-infrared spectroscopy and machine learning models for indirect assessment of grape ripening indicators

Abstract The assessment of grape ripeness is pivotal for optimizing harvest timing and ensuring high-quality fruit production. Traditional methods, relying on manual sampling and chemical analysis, are laborious and expensive. This study proposes an innovative approach combining Visible/Near-Infrare...

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Bibliographic Details
Main Authors: Osama Elsherbiny, Salah El-Hendawy, Salah Elsayed, Abdallah Elshawadfy Elwakeel, Abdullah Alebidi, Xianlu Yue, Wael Mohamed Elmessery, Hoda Galal
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Chemometrics
Decision tree
Fruit quality
Gradient boosting regression
Precision agriculture
Online Access:https://doi.org/10.1038/s41598-024-81694-3
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https://doi.org/10.1038/s41598-024-81694-3

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