Rice plays a pivotal role in global food security, particularly for Asian populations. However, its production is significantly threatened by insect pests, with Chilo suppressalis being a major pest in Asian rice-growing regions. Therefore, developing accurate predictive models for C. suppressalis outbreaks is essential. This study presents a novel time series forecasting model (named ESD-TripleStream) for C. suppressalis population dynamics based on a multi-stream structure, which addresses the limitations of existing approaches, which often omit the further decomposability of and the timestamp information in the time series. This model integrates Exponential Smoothing Decomposition (ESD) to separate the trend and seasonal components of time series data, along with a temporal feature stream to form a three-stream network to capture multi-scale periodic patterns and temporal dependencies. For our evaluation, we collected and constructed a novel dataset, referred to as HNRP-6R, which includes rice pest monitoring data from the past two decades (2000–2022) alongside 13 meteorological factors across six key rice producing regions in Hunan Province, southern China. ESD-TripleStream was evaluated across short-term and medium-term C. suppressalis population prediction scales using HNRP-6R, demonstrating state-of-the-art performance. Specifically, in short-term prediction, ESD-TripleStream achieved a 31.8% reduction in Mean Squared Error (MSE) and 26.55% reduction in Mean Absolute Error (MAE) compared to the PatchMLP model, while outperforming the transformer-based TimeXer by 14.43% in MSE and 9.8% in MAE. For medium-term prediction, ESD-TripleStream has both MSE and MAE significantly lower than those of baseline models such as P-sLSTM and xPatch. Furthermore, generalization tests on Nilaparvata lugens (N. lugens) population prediction demonstrated the model’s adaptability to diverse pest dynamics.

Seed viability fundamentally determines crop establishment, stress resilience, and yield stability in pepper (Capsicum annuum L.), yet conventional assessment remains destructive, labor-intensive, and poorly scalable, while existing spectral learning approaches largely lack physiological interpretability, limiting their reliability for industrial seed quality management. Here, we present ViaNet, a lightweight, interpretable deep hyperspectral classification framework for 1038 naturally aged pepper seeds labeled via standardized 14-day germination tests. ROI-averaged hyperspectral reflectance vectors are modeled as a binary classification task, and ViaNet integrates Successive Projections Algorithm (SPA)-based wavelength sparsification with Efficient Channel Attention (ECA)-driven spectral weighting within a compact 1D-CNN architecture, enabling physiologically grounded feature learning under strict computational constraints. The model achieves recall for germinable seeds (79.75%) and outperforms classical machine learning methods. In addition, ViaNet consistently highlights reproducible spectral bands associated with natural-aging-related biochemical changes as reported in the literature (e.g., carotenoid-related absorption features in the near-UV region). By coupling spectral feature selection with attention-guided wavelength focusing, ViaNet establishes a closed analytical chain from spectral compression to physiologically interpretable inference. This framework balances predictive accuracy, interpretability, and deployability and provides a scalable, non-destructive, and biologically informed paradigm for hyperspectral seed viability assessment.