基于环境时间序列的葡萄园病害风险事件式早期预警
Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series
AI总结：
本文提出基于事件的环境时间序列预警方法，将葡萄园病害预测重构为未来3-7天风险期转变预测，评估多种模型，验证其支持短时预警并优于传统每日分类。
摘要：
基于环境观测对葡萄园病害风险进行准确早期预警，对于及时干预和更可持续的作物保护至关重要。然而，许多现有研究将病害预测表述为每日存在性分类，这可能偏向于持续性驱动的预测，并且对可操作的短时预警支持有限。在本文中，我们提出了一种基于事件的方法，用于从环境时间序列中对葡萄园病害风险进行早期预警，并通过葡萄园案例研究对其进行评估。该任务不是预测每日病害状态，而是重新表述为预测未来3-7天窗口内进入标注病害风险期的转变。为减少二元标签中短暂中断造成的碎片化，新事件仅在最小无病害间隔之后定义。这种表述鼓励模型捕捉与即将到来的风险期相关的环境前兆，而非仅仅再现时间持续性。利用多年农业气象数据，我们构建了输入表示，通过循环时间编码捕捉湿度动态、降雨累积、温度变异和季节结构。我们评估了经典机器学习和深度学习的代表性方法，包括XGBoost、长短期记忆（LSTM）网络和时间卷积网络（TCN），使用标准分类指标和面向事件的早期预警协议。结果表明，基于事件的表述支持实用的短时预警，而所比较的模型在事件召回率、提前时间和误报行为之间表现出不同的权衡。总体而言，该研究强调了问题表述在环境时间序列学习中的重要性，并展示了基于事件的预测对葡萄园病害预警系统的价值。
英文摘要：
Accurate early warning of vineyard disease risk from environmental observations is essential for timely intervention and more sustainable crop protection. However, many existing studies formulate disease prediction as daily presence classification, which can favor persistence-driven predictions and provide only limited support for actionable short-horizon warning. In this paper, we present an event-based approach for early warning of vineyard disease risk from environmental time series and evaluate it through a vineyard case study. Rather than predicting daily disease status, the task is reformulated to predict transitions into annotated disease-risk periods within a future window of 3-7 days. To reduce fragmentation caused by short interruptions in the binary labels, new events are defined only after a minimum disease-free gap. This formulation encourages models to capture environmental precursors associated with upcoming risk periods instead of merely reproducing temporal persistence. Using multi-year agro-meteorological data, we construct input representations that capture humidity dynamics, rainfall accumulation, temperature variability, and seasonal structure through cyclic temporal encoding. We evaluate representative methods from classical machine learning and deep learning, including XGBoost, Long Short-Term Memory (LSTM) networks, and Temporal Convolutional Networks (TCNs), using both standard classification metrics and an event-oriented early warning protocol. The results show that the event-based formulation supports practical short-horizon warning, while the compared models exhibit distinct trade-offs between event recall, lead time, and false-alert behavior. Overall, the study underscores the importance of problem formulation in environmental time-series learning and demonstrates the value of event-based prediction for vineyard disease warning systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
