Hongrui Wang, Gaurav D Moghe, Al P Kovaleski, Markus Keller, Timothy E Martinson, A Harrison Wright, Jeffrey L Franklin, Andréanne Hébert-Haché, Caroline Provost, Michael Reinke, Amaya Atucha, Michael G North, Jennifer P Russo, Pierre Helwi, Michela Centinari, Jason P Londo
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...
February 2024: Horticulture Research