Early Pest-Stress Detection and Pest-Type Discrimination in Maize Using Multi-Source Physiological and Spectral Data with Stage-Consistency-Constrained Feature Selection

Maize is globally important for food production, while insect pests adversely affect its yield and quality. Multi-source physiological and spectral responses can support early pest identification, but variable correlations and temporal changes in discriminative effects may introduce redundant or stage-specific features, destabilize selection, and increase model complexity. This study proposed a stage-consistency-constrained feature selection method (SCCFS), in which stage consistency denotes the persistence of discriminative ability across the 2–12 h observation groups. Jointing-stage maize plants were assigned to healthy control, mechanical damage, leaf-feeding pest, or stem-boring pest treatments. Gas exchange, chlorophyll fluorescence, spectral indices, and 400–1000 nm ASD reflectance provided 613 candidate variables for pest detection and pest-type identification. SCCFS evaluated overall discrimination, temporal persistence, and resampling repeatability, and determined compact subsets through forward selection and the one-standard-error rule. Using shrinkage linear discriminant analysis (LDA), SCCFS achieved Macro-F1 values of 0.9639 for pest detection and 0.9926 for pest-type identification. Out-of-fold (OOF) Shapley additive explanations (SHAP) analysis of the radial-basis-function support vector machine (SVM-RBF) models identified relative electron transport rate (rETR), transpiration rate (Tr), operating efficiency of photosystem II photochemistry (Fq′/Fm′), and stomatal conductance (Gs) as key contributors to pest detection, and intercellular CO2 concentration (Ci), relative electron transport rate (rETR), normalized difference vegetation index (NDVI), and photochemical reflectance index (PRI) as key contributors to pest-type identification. For pest detection and pest-type identification, the Nogueira stability indices were 0.6946 and 0.8227, respectively, and the mean absolute Spearman correlation coefficients were 0.3016 and 0.4625, respectively. SCCFS retained pest diagnostic performance while yielding compact, temporally persistent, and low-redundancy subsets, providing a compact variable representation for maize pest identification under the tested greenhouse conditions.