Accurate Large-Scale Forecasting of Disease in Mountainous Regions: Integrating Multi-Source Habitat Information with Spatiotemporal Phenological Corrections

Abstract

Accurate large-scale forecasting of tea anthracnose (TA) (Gloeosporium theae-sinensis Miyake) is crucial for tea cultivation disease management. Multi-source habitat information, including meteorological, geographical, and remote sensing data, provides a critical foundation for disease forecasting. However, the complex microclimatic conditions of mountainous regions lead to highly heterogeneous spatiotemporal phenological response patterns in tea plants, creating significant challenges for extracting and utilizing habitat factors in disease forecasting models. To address these challenges, this study proposes a novel forecasting approach for TA by integrating multi-source habitat information with spatiotemporal phenological corrections. Using survey data on TA collected in Zhejiang Province from 2016 to 2020, a spatiotemporal phenological correction strategy based on accumulated growing degree days (AGDD) was developed. This strategy facilitated the alignment of AGDD-calibrated multi-source habitat data to construct a comprehensive feature dataset for disease forecasting. A forecasting model was subsequently developed through the application of the Relief-F algorithm for feature selection, combined with representative machine learning techniques, including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), and Naive Bayes (NB). The results demonstrated that the inclusion of AGDD-aligned multi-source features significantly enhanced forecasting performance, achieving an overall accuracy (OA) of 77% and a kappa coefficient of 0.65, outperforming traditional calendar-aligned approaches and single-meteorological-factor models. From a spatiotemporal heterogeneity perspective, this study elucidated the response characteristics of multi-source habitat factors under varying geographical and terrain conditions using the AGDD-aligned phenological correction strategy. Furthermore, the integration of remote sensing data, which reflects the physiological state of tea plants, with meteorological and geographical factors substantially improved the comprehensiveness and precision of TA forecasting. These findings highlight the importance of incorporating spatiotemporal phenological corrections and multi-source habitat data for advancing disease forecasting methodologies in complex agricultural landscapes.

Keywords: Tea anthracnose, multi-sources habitat factors, spatiotemporal phenological corrections, AGDD, disease forecasting model