GrasshopperML: A mechanism-guided machine learning framework with combinatorial optimization for grasshopper risk warning

Grasshoppers pose a serious threat to agriculture, livestock and grassland ecosystem, necessitating accurate risk warning. Existing methods often overlook grasshopper ecological mechanisms and the impacts of class imbalance, while lacking combinatorial optimization across machine learning (ML) pipelines, which weakens interpretability, accuracy and performance. To address these challenges, this study proposes GrasshopperML, a mechanism-guided ML framework with pipeline-level combinatorial optimization, integrating multi-source remote sensing and meteorological data. Its three key innovations are: 1) a high-dimensional environmental feature set reflecting overlap and spatiotemporal heterogeneity of grasshopper developmental stages (GDSs), incorporating precipitation, temperature, vegetation, soil, and topography for ecologically grounded modeling; 2) GDSTree, a new mechanism-guided feature selection (FS) algorithm that identifies GDS-specific key factors to enhance ecological interpretation and prediction accuracy; 3) a pipiline-level combinatorial optimization strategy exploring optimal combinations across data preprocessing, FS, and prediction modeling. To tackle class imbalance, the framework evaluates 40 combinations of five adaptive under-sampling (AUS) methods and eight ML algorithms. Experiments based on 2022 field survey data from Hulunbuir, China, show the optimal AUS-GDSTree-MLA combination outperforms five advanced FS algorithms, achieving a 62% feature reduction, 90.83% balanced accuracy, and 88.40% prediction performance. A 2023 risk map further validates GrasshopperML's practical utility, with 85.19% of presence sites falling within predicted moderate/high risk areas. This study provides an AI-based solution that integrates ecological knowledge, mechanism-guided FS, and ML pipeline optimization for advanced pest risk warning.