用可迁移的潜在表示预测葡萄藤抗寒性，跨区域更准。
Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations
- 通过学习品种与地区嵌入，捕捉区域差异特征。
- 在6个北美区域数据上优于现有方法，跨区预测更准。
- 支持零样本和少样本迁移，适合数据稀缺地区。
准确预测木本植物的每日抗寒能力对冻害易发地区至关重要，因低温会损伤休眠芽并降低产量。现有生物物理、混合及深度学习模型在本地数据上表现优异，但多局限于特定地点。冷硬性数据稀缺，且缺乏将预测能力迁移到新区域和品种的系统方法，限制了其广泛应用，尤其在数据匮乏地区。为此，我们提出一种抗寒性预测框架，通过学习嵌入来捕获区域特异性变化，构建可迁移的潜在表示。为实现对未见区域的预测，我们基于（1）品种与生长区域的文本描述，以及（2）有限的历史观测数据推断嵌入，支持零样本与少样本迁移。在涵盖北美六个区域的数据集上实验表明，该方法持续优于当前最优抗寒性预测方法，在预测精度和向数据稀疏地区迁移方面均有显著提升。
原文摘要 · Abstract (English)
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.
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