发表机构
Oregon State University; Washington State University（俄勒冈州立大学; 华盛顿州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有抗寒性预测模型仅适特定区域的局限，提出基于多模态潜在表示的框架，可实现跨区域抗寒性预测，在北美六区域数据集上性能优于现有方法。
AI 中文摘要
木本植物抗寒性的每日准确预测在易受低温损害休眠芽并降低季节产量的地区至关重要。现有生物物理模型、混合模型及深度学习模型在基于本地数据训练时已展现高预测精度，但大多仅适用于特定站点。抗寒性数据的有限可用性，加上缺乏将抗寒性预测迁移至新区域和品种的系统性方法，限制了这些方法的更广泛应用与实际效用，尤其在数据稀缺地区。为解决这些局限，我们提出一种抗寒性预测框架，该框架通过学习嵌入捕获区域特异性变异，从而学习可迁移的潜在表示。为实现对未见过区域的预测，我们从两方面推断嵌入：（1）品种和生长区域的文本描述；（2）有限的历史观测，支持零样本和少样本迁移。对北美六个区域数据集的实验表明，我们的方法始终优于最先进的抗寒性预测方法，预测更准确，且大幅提升了向数据稀缺区域的迁移效果。
英文摘要
Accurate daily predictions of cold hardiness in woody plants are critical in regions where freezing temperatures can damage dormant buds and reduce seasonal yield. Existing biophysical, hybrid, and deep learning models have shown high predictive accuracy when trained on local data but remain largely site-specific. The limited availability of cold hardiness data, coupled with the lack of principled methods for transferring cold hardiness predictions to new regions and cultivars, has limited the broader adoption and practical utility of these approaches, particularly in data-scarce regions. To address these limitations, we propose a cold hardiness prediction framework that learns a transferable latent representation by capturing region-specific variation through learned embeddings. To enable prediction in previously unseen regions, we infer embeddings from (1) text descriptions of the cultivar and growing region, and (2) limited historical observations, supporting both zero-shot and few-shot transfer. Experiments on datasets from six regions across North America demonstrate that our approach consistently outperforms state-of-the-art cold hardiness prediction methods, yielding more accurate predictions and substantially improving transfer to data-scarce regions.
