AI Insight
This study introduces an event-based machine learning approach to predict vineyard disease risk 3-7 days in advance using environmental time series data. Rather than classifying daily disease presence, the method predicts transitions into disease-risk periods by identifying environmental precursors, comparing performance across XGBoost, LSTM, and Temporal Convolutional Networks. The event-based formulation demonstrates improved practical utility for early warning systems compared to traditional daily classification approaches, though different models show distinct trade-offs between detection accuracy, warning lead time, and false alerts.
Why it matters
Early disease risk prediction enables timely, targeted interventions in vineyards, potentially reducing pesticide use and supporting more sustainable agricultural practices. The event-based framework presented could be adapted to other crop disease warning systems and environmental monitoring applications where anticipating state transitions is more valuable than tracking daily status.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: 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.
Source: Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series