发表机构
School of Electrical Engineering and Computer Science, Oregon State University; School of Electrical Engineering and Computer Science, Washington State University; Irrigated Agriculture Research and Extension Center, Washington State University（俄勒冈州立大学电气与计算机工程学院; 华盛顿州立大学电气与计算机工程学院; 华盛顿州立大学灌溉农业研究与推广中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出多任务学习框架，利用循环神经网络从天气数据预测葡萄抗寒性，通过共享品种间信息，在稀疏标签下提升预测精度，并验证了同时学习抗寒性与芽破任务的优势。
AI 中文摘要
我们提出了一个多任务学习（MTL）在有限数据且时间标签稀疏的情况下进行时间过程建模的真实世界案例研究。具体而言，我们研究了多任务学习在预测葡萄抗寒性这一重要农业问题中的应用，抗寒性是指发生致命冻害的温度。抗寒性随天气变化而变化，且难以在田间直接测量。因此，种植者依赖预测来决定何时采取昂贵的防霜措施。我们应用循环神经网络（RNN）从时间序列天气数据中进行每日抗寒性预测。一个主要挑战是抗寒性响应因葡萄品种而异，且每个品种的真实数据在时间上稀疏且有限。为应对这一挑战，我们研究了多任务学习（MTL）方法来组合数据，其中不同任务对应不同品种。我们开发了多种MTL架构，并在MTL和迁移学习设置中对其进行了评估。我们的结果显示架构之间存在显著差异，且某些架构能够持续优于单任务学习和最先进的科学模型。此外，我们展示了在性质不同但相关的芽破预测任务上也有类似结果。进一步地，通过一个同时学习两个任务的单一MTL模型，芽破和抗寒性的预测精度均得到了提升。
英文摘要
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.
