Early Warning of Anthracnose on Illicium verum Through the Synergistic Integration of Environmental and Remote Sensing Time Series Data

Abstract

Highlights: What are the main findings? Proposed an early warning framework for anthracnose on I. verum by integrating high-frequency environmental data (meteorological and topographic) with Sentinel-2 time-series imagery. Developed an Attention-based Time-Aware LSTM (At-T-LSTM) model that effectively captures temporal dependencies and inter-feature interactions, achieving high accuracy in spatial delineation and temporal early detection. What are the implications of the main findings? The framework enables reliable early detection of anthracnose despite sparse optical observations and weak early-stage spectral responses in cloudy and rainy regions. Provides a practical and generalizable tool for precision forestry, supporting the timely risk assessment and sustainable management of I. verum and other forest diseases. Anthracnose on Illicium verum Hook.f (I. verum) significantly affects the yield and quality of I. verum, and timely detection methods are urgently needed for early control. However, early warning is difficult due to two major challenges, including the sparse availability of optical remote sensing observations due to frequent cloud and rain interference, and the weak spectral responses caused by infestation during early stages. In this article, a framework for early warning of anthracnose on I. verum that combines high-frequency environmental (meteorological and topographical) data and Sentinel-2 remote sensing time-series data, along with a Time-Aware Long Short-Term Memory (T-LSTM) network incorporating an attentional mechanism (At-T-LSTM) was proposed. First, all available environmental and remote sensing data during the study period were analyzed to characterize the early anthracnose outbreaks, and sensitive features were selected as the algorithm input. On this basis, to address the issue of unequal temporal lengths between environmental and remote sensing time series, the At-T-LSTM model incorporates a time-aware mechanism to capture intra-feature temporal dependencies, while a Self-Attention layer is used to quantify inter-feature interaction weights, enabling effective multi-source features time-series fusion. The results show that the proposed framework achieves a spatial accuracy (F1-score) of 0.86 and a temporal accuracy of 83% in early-stage detection, demonstrating high reliability. By integrating remote sensing features with environmental drivers, this approach enables multi-feature collaborative modeling for the risk assessment and monitoring of I. verum anthracnose. It effectively mitigates the impact of sparse observations and significantly improves the accuracy of early warnings.