Vinay Pagay

Lecturer

University of Adelaide

Australia

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Vinay Pagay is a Lecturer at the University of Adelaide in Australia. His research areas include plant response to environmental stress, precision agriculture, and the application of machine learning in viticulture. Recent articles authored by Pagay focus on topics such as anthocyanin biosynthesis in grapes, non-contact monitoring of plant health, and data-driven irrigation scheduling to enhance crop efficiency.

Articles (20)

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2025

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2022

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2022

Collaborators (11)

Deepak Gautam

Lecturer

Charles Darwin University

AUSTRALIA

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Stephen Tyerman

Emeritus Professor

University of Adelaide

AUSTRALIA

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Withawat Withayachumnankul

AUSTRALIA

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Sunita Ramesh

Flinders University

AUSTRALIA

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William Price

Professor of Medical Imaging Physics

University of Western Sydney

AUSTRALIA

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Leigh Schmidtke

Charles Sturt University

AUSTRALIA

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Franco Meggio

University of Padova

ITALY

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Bertram Ostendorf

A/Prof

University of Adelaide Faculty of Sciences

AUSTRALIA

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Christopher Ford

Associate Professor

University of Adelaide

AUSTRALIA

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David Jeffery

University of Adelaide Waite Campus

AUSTRALIA

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Paolo Sivilotti

University of Udine

ITALY

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Vinay Pagay

Research Interests

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Articles (20)

Non-Contact Monitoring of Plant Leaf Water Status Using Terahertz Waves

VvBBX32 responds to high temperatures and mediates anthocyanin biosynthesis in ‘Merlot’ grape (Vitis vinifera L.) berries

Heat Stress Downregulates Photosystem I Redox State on Leaf Photosynthesis in Grapevine

Vineyard Spatial Structures of Key Soil, Vine and Fruit Metrics Are Hierarchically Associated

Can Multi-Temporal Vegetation Indices and Machine Learning Algorithms Be Used for Estimation of Groundnut Canopy State Variables?

Evaluating the potential of high-resolution hyperspectral UAV imagery for grapevine viral disease detection in Australian vineyards

Data-driven irrigation scheduling increases the crop water use efficiency of Cabernet Sauvignon grapevines

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

Evidence of Bi-Directional Volatile-Mediated Communication between Drought-Stressed and Well-Watered Grapevines (Vitis vinifera L.)

Grape Heterogeneity Index: Assessment of Overall Grape Heterogeneity Using an Aggregation of Multiple Indicators

Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine

Evaluating the Potential of High-Resolution Visible Remote Sensing to Detect Shiraz Disease in Grapevines

Detecting Grapevine Virus Infections in Red and White Winegrape Canopies Using Proximal Hyperspectral Sensing

Corrigendum to “Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra” [Food Chemistry 403 (2023) 134321] (Food Chemistry (2023) 403, (S030881462202283X), (10.1016/j.foodchem.2022.134321))

Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra

Characterisation of internal oxygen concentration of strawberry (

Insights into Long-Term Acclimation Strategies of Grapevines (<i>Vitis vinifera</i> L.) in Response to Multi-Decadal Cyclical Drought

Facilitating mapping and understanding of within‐vineyard variation in fruit composition using data pooled from multiple vineyards

Stress-Induced Volatile Emissions and Signalling in Inter-Plant Communication

Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses

Collaborators (11)

Deepak Gautam

Stephen Tyerman

Withawat Withayachumnankul

Sunita Ramesh

William Price

Leigh Schmidtke

Franco Meggio

Bertram Ostendorf

Christopher Ford

David Jeffery

Paolo Sivilotti

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