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Life sciences · Preprint
arXiv · September 8, 2026
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This is an unpublished case study demonstrating that multi-task learning architectures can outperform single-task models and scientific baselines for predicting grape cold hardiness and budbreak from sparse time-series weather data. The work is exploratory and methodological; it does not report effect sizes, confidence intervals, or field-validated agronomic impact, and has not undergone peer review.
Preprint. Grape plant cultivars with temporally sparse ground-truth cold-hardiness measurements. Intervention: Multi-task learning recurrent neural networks for daily cold-hardiness prediction from time-series weather data. Compared with: Single-task learning and state-of-the-art scientific models.
Certain MTL architectures consistently outperformed single-task learning in cold-hardiness prediction MTL approaches exceeded state-of-the-art scientific models for the task A single MTL model that jointly learns both budbreak and cold-hardiness prediction achieved improved accuracy on both tasks
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A proof-of-concept case study applying machine learning to an agricultural prediction problem with limited labeled data; lacks clinical validation, peer review, and comparison against established baselines with reported effect sizes.
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We present a real-world case study of multi-task learning (MTL) for temporal process modeling from limited data with temporally sparse labels. Specifically, we investigate multi-task learning for the important agricultural problem of predicting grape cold hardiness, which is the temperature at which lethal freezing occurs. Cold hardiness changes in response to weather and is difficult to measure directly in the field. Thus, growers rely on predictions to decide when to apply costly frost mitigation measures. We apply recurrent neural networks (RNNs) for daily cold-hardiness prediction from time series weather data. A major challenge is that the cold hardiness response varies across plant cultivars and ground-truth data for each cultivar is temporally sparse and limited. To address this challenge, we investigate multi-task learning (MTL) approaches for combining data, where different tasks correspond to different cultivars. We develop a variety of MTL architectures and evaluate them in both MTL and transfer learning settings. Our results show significant differences between architectures and that certain architectures are able to consistently outperform single-task learning and state-of-the-art scientific models. Additionally, we show similar results for the qualitatively different, but related, task of budbreak prediction. Further, improved accuracy for budbreak and cold hardiness is achieved by a single MTL model that simultaneously learns both tasks.
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