Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations

2026-08-31

AI Summary

本文提出了一种通过学习多模态潜在表示来解决跨区域葡萄藤耐寒性预测问题的方法，利用文本描述和有限历史数据进行迁移学习。

Abstract

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.

Problem

• cold hardiness
• data-scarce regions
• transferability

Innovation

• transferable latent representation
• learned embeddings
• zero-shot and few-shot transfer
• data-scarce regions