Abstract:

Botryosphaeria laricina (larch shoot blight) was first identified in 1973 in Jilin Province, China. The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were no effective controls or quarantine methods. At present, it shows a spreading trend, but most research can only conduct physiological analyses within a relatively short period, combining individual influencing factors. Nevertheless, methods such as neural network models, ensemble learning algorithms, and Markov models are used in pest and disease prediction and forecasting. However, there may be fitting issues or inherent limitations associated with these methods. This study obtained B. laricina data at the county level from 2003 to 2021. The dataset was augmented using the SMOTE algorithm, and then algorithms such as XGBoost were used to select the significant features from a combined set of 12 features. A new stacking fusion model has been proposed to predict the status of B. laricina. The model is based on random forest, gradient boosted decision tree, CatBoost and logistic regression algorithms. The accuracy, recall, specificity, precision, F₁ value and AUC of the model reached 90.9%, 91.6%, 90.4%, 88.8%, 90.2% and 96.2%. The results provide evidence of the strong performance and stability of the model. B. laricina is mainly found in the northeast and this study indicates that it is spreading northwest. Reasonable means should be used promptly to prevent further damage and spread.

Key words: Botryosphaeria laricina, Data augmentation, Feature selection, Stacking, Status of spread