Diamondback moth (DBM, Plutella xylostella) outbreaks are shaped by delayed meteorological conditions, yet most forecasting models compress weather into a few monthly summaries and provide limited ecological interpretation. We propose MeteoSCOPE, an ontology-aware sparse Perceiver framework for interpretable, multi-horizon retrospective forecasting of DBM abundance from historical pest records and rich meteorological descriptors. Each feature-lag value is encoded as a token carrying feature identity, ecological group, descriptor type, lag position, and seasonal information; in the rich setting, 138 descriptors across 12 months yield 1656 tokens per sample. Sparse cross-attention compresses these tokens into a compact latent representation, while horizon-specific queries produce one- to four-month-ahead forecasts. Attention tensors and a common-plus-residual branch are aggregated into feature-, group-, descriptor-, lag-, horizon-, and residual-level explanations. Using DBM records from Huiyang and Shantou, Guangdong, MeteoSCOPE achieved the strongest overall retrospective performance, with robust gains at Shantou and metric-dependent gains at Huiyang. The explanations identified pest history as the leading attended group at both sites and surfaced site-specific secondary attributions for soil moisture, weather state, wind, soil temperature, and humidity, treated as model evidence rather than causal ecological effects and corroborated by independent occlusion and KernelSHAP analyses. Strict zero-shot cross-site transfer degrades substantially, so prospective field validation and broader multi-site testing remain required before operational deployment. MeteoSCOPE thus provides a transferable methodological framework (not a deployable forecaster) for interpretable analysis of high-dimensional agricultural time series.
Zhang et al. (Thu,) studied this question.