Wang Hongrui, Moghe Gaurav D, Kovaleski Al P, Keller Markus, Martinson Timothy E, Wright A Harrison, Franklin Jeffrey L, Hébert-Haché Andréanne, Provost Caroline, Reinke Michael, Atucha Amaya, North Michael G, Russo Jennifer P, Helwi Pierre, Centinari Michela, Londo Jason P
School of Integrative Plant Science, Horticulture Section, Cornell AgriTech, Cornell University, Geneva, NY 14456, USA.
School of Integrative Plant Science, Plant Biology Section, Cornell University, Ithaca, NY 14850, USA.
Hortic Res. 2023 Dec 29;11(2):uhad286. doi: 10.1093/hr/uhad286. eCollection 2024 Feb.
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.
准确且实时地监测葡萄的抗冻性对于凉爽气候葡萄种植区葡萄产业的可持续发展至关重要。然而，由于测量的复杂性，现场数据有限。当前的预测模型在不同气候条件下表现不佳，这限制了这些方法的大规模应用。我们整合了北美多个地区的葡萄抗冻性数据，并使用自动化机器学习引擎AutoGluon，基于每小时温度衍生特征和品种特征生成了一个预测模型。通过AutoGluon和SHAP（SHapley加性解释）值对特征重要性进行了量化。对最终模型在不同气候条件下的性能进行了评估，并与先前的模型进行了比较。最终模型在模型测试期间的总体均方根误差为1.36°C，在所有测试地区使用三个测试品种时，其表现优于之前的两个模型。两种特征重要性量化方法确定了五个共同的关键特征。对这些特征的详细分析表明，该模型在训练过程中充分提取了一些生物学机制。最终模型名为NYUS.2，在2022 - 23年休眠季节与之前的两个模型一起作为基于R shiny的应用程序进行了部署，首次实现了对北美葡萄抗冻性的大规模实时模拟。
