Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations
2026-08-31 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors developed a new method to predict how well woody plants can survive freezing temperatures in different places. Their approach learns special patterns that capture local differences by using information about the plant types and regions. This lets their model make good predictions even in places where there isn’t much data available. Tests in six North American regions show their method works better than existing models, especially when data is limited.
cold hardinesswoody plantslatent representationembeddingzero-shot transferfew-shot transferplant cultivarsregion-specific variationpredictive modelingdata scarcity
Authors
William Solow, Paola Pesantez-Cabrera, Markus Keller, Lav Khot, Sandhya Saisubramanian, Alan Fern
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.