A continuous wavelet transform-enhanced transformer model for spectroscopic detection of bacterial wilt in tomato at early infection stages

Introduction Tomato ( Solanum lycopersicum L .) bacterial wilt is a highly destructive soil-borne disease whose early-stage infections are asymptomatic or visually indistinguishable, limiting the effectiveness of conventional diagnostic methods. Methods This study developed a Continuous Wavelet Transform-enhanced Transformer framework (CWT-STNet) for early-stage detection of bacterial wilt using hyperspectral data collected from tomato canopies under controlled inoculation conditions. Infection severity was categorized into grades 0 to 4, with levels 1–2 defined as the early infection stage. Raw spectral data were fused with multi-scale wavelet features extracted using Continuous Wavelet Transform (CWT), and the effects of different scale combinations on model performance were systematically evaluated. CWT-STNet was compared with one-dimensional convolutional neural networks (1D-CNNs), random forests (RF), and support vector machines (SVM). SHapley Additive exPlanations (SHAP) were further used to interpret model predictions and identify disease-sensitive spectral features. Results The proposed CWT-STNet framework achieved an overall accuracy of 96.77% across all infection levels and an early-stage detection accuracy of 93.60%. In comparison, models relying only on raw spectra achieved 84.15% accuracy in distinguishing early disease severity. Incorporating CWT-derived multi-scale features improved the performance of all evaluated models, while ablation analysis confirmed the importance of combining raw spectra with multi-scale wavelet features. SHAP analysis identified spectral features around 869.0 nm and the 1160–1190 nm region as particularly important for early bacterial wilt detection. Discussion The identified spectral regions were associated with physiological and structural changes related to bacterial wilt infection, including alterations in plant water status, leaf cellular structure, and pigment-related responses. These findings demonstrate that the proposed CWT-STNet framework provides a robust, interpretable, and non-destructive approach for early detection of tomato bacterial wilt and has potential for precision disease monitoring.