• Keller, Markus
• Hébert-Haché, Andréanne
• Franklin, Jeffrey L.
• Provost, Caroline
• Kovaleski, Al P.
• Wright, A. Harrison
• Martinson, Timothy E.
• Reinke, Michael
• North, Michael G.
• Russo, Jennifer P.
• Moghe, Gaurav D.
• Helwi, Pierre
• Centinari, Michela
• Wang, Hongrui
• Londo, Jason P.

Abstract

<jats:title>Abstract</jats:title><jats:p>Accurate and real-time monitoring of grapevine freezing tolerance is crucial for the sustainability of the grape industry in cool climate viticultural regions. However, on-site data are limited due to the complexity of measurement. Current prediction models underperform under diverse climate conditions, which limits the large-scale deployment of these methods. We combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automated machine learning engine. Feature importance was quantified by AutoGluon and SHAP (SHapley Additive exPlanations) value. The final model was evaluated and compared with previous models for its performance under different climate conditions. The final model achieved an overall 1.36°C root-mean-square error during model testing and outperformed two previous models using three test cultivars at all testing regions. Two feature importance quantification methods identified five shared essential features. Detailed analysis of the features indicates that the model has adequately extracted some biological mechanisms during training. The final model, named NYUS.2, was deployed along with two previous models as an R shiny-based application in the 2022–23 dormancy season, enabling large-scale and real-time simulation of grapevine freezing tolerance in North America for the first time.</jats:p>

Topics

• impedance spectroscopy
• simulation
• machine learning