Intelligent Monitoring of Diseases and Insect Pests in Rice and Wheat: A Review of Multimodal Data Fusion and Early Warning Systems

Intelligent monitoring of diseases and insect pests in rice and wheat has evolved from handcrafted features and conventional machine learning to deep learning, multimodal data fusion, and time-series forecasting. This review compares data acquisition and representation, unimodal recognition, multimodal fusion, temporal prediction, and field generalization with respect to data requirements, task outputs, application contexts, and the strength of supporting evidence. Conventional machine learning remains valuable for small datasets, variable interpretation, and baseline comparisons, whereas deep learning extends monitoring from classification to detection, segmentation, pest counting, and severity estimation. Multimodal and temporal models further integrate phenotypic, physiological, environmental, and pest-monitoring information to predict future risk. However, many reported gains are weakened by inadequate spatiotemporal alignment, non-independent data partitioning, limited missing-modality tests, and insufficient cross-location and cross-year validation. Future research should prioritize standardized multisite, multiyear datasets; label-efficient, mechanistically informed, and trustworthy fusion methods; lightweight deployment; and prospective field trials that link model outputs to management decisions and production outcomes.