Forecasting of large-scale tea anthracnose in mountainous region of China though integration of multi-source habitat information with spatiotemporal phenological corrections

Abstract

Accurate large-scale forecasting of tea anthracnose (TA) (Colletotrichum camelliae) is crucial for tea cultivation disease management. TA infection exhibits a strong synchrony with phenological development regulated by accumulated growing degree days (AGDD). However, the complex microclimatic conditions of mountainous regions lead to highly heterogeneous spatiotemporal phenological response patterns in tea plants, posing significant challenges to the effective extraction and utilization of habitat factors in disease forecasting models. To address these issues, 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 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 strategy combining RF with AGDD-aligned multi-source features significantly enhanced forecasting performance, achieving an average overall accuracy (OA) of 77 % and an average kappa coefficient of 0.65. This approach outperformed conventional methods using either calendar-aligned features or single meteorological factors combined with machine learning algorithms. 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.