Abstract: Rice blast disease, caused by Magnaporthe grisea, has posed a serious threat to global rice production in recent years. However, the conventional diagnostic techniques cannot fully detect the visible symptoms during the asymptomatic incubation period. In this study, a rapid and non-destructive diagnostic framework was established for the symptomatic and pre-symptomatic stages. Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) was also used to detect early metabolic and structural changes in the infected rice leaves. Spectral data ( 2 000-900 cm^-1) of the sampling sites were collected from the healthy (H0-H3), symptomatic (D0-D4), and incubation-stage leaves using FTIR-PAS with depth-resolved scanning. The moving mirror velocity was also adjusted to 0.3, 0.4, and 0.6 cm/s. Background signals (D0/H0) were subtracted to isolate the pathogen-specific spectral features. Key absorption bands, 1 050 cm^-1 (C-O stretching), 1 550 cm^-1 (amide II), and 1 650 cm^-1 (amide I), were analyzed to identify the disease-induced alterations. According to the intensity ratio of 1 650/1 050 cm^-1 (R_1650/1050), the diagnostic model was developed and then validated using Principal Component Analysis (PCA), Pearson correlation, and spatial gradient mapping. The original spectra revealed that in healthy samples (H1-H3), there were much lower absorption peak intensities at 1 550 and 1 650 cm^-1 of the shallow-layer spectra at 0.4 and 0.6 cm/s, compared with the high absorption intensity in the deep-layer spectra at 0.3 cm/s. The 0.3 cm/s measurement also penetrated the cellular layer to detect the amide groups, while 0.4 and 0.6 cm/s were confined to the epidermis, thus lacking amide-related signals. In diseased samples (D1-D3), the absorption intensity at 1,550 and 1,650 cm^-1 in 0.4 cm/s spectra increased significantly, closely resembling the 0.6 cm/s profiles. Furthermore, the absorption intensity of 0.3 cm/s spectra was progressively intensified and converged with 0.6 cm/s data, as the sampling sites approached the lesion center (D1 to D3, and D4 as the center). The rice blast infection reduced the epidermal thickness, thus leading to the spectral similarity between shallow and deep layers. There was a positive correlation between the degree of thinning and pathogen load. At the same time, the Background-subtracted spectra demonstrated that the weak absorption peaks were observed at 1,550 and 1,650 cm^-1 in the healthy samples, comparable to baseline levels, whereas the diseased samples exhibited the strong peaks in the 86.7% spectral correlation with Magnaporthe oryzae. The pathogen-specific signals were verified in the infected leaves via FTIR-PAS. Furthermore, the magnitude of the R_1650/1050 ratio was identified as a diagnostic marker for the rice blast. The total accuracies of 81%, 87%, and 77% were achieved with the F1-scores of 0.84, 0.91, and 0.80 at thresholds of 0.4, 0.5, and 0.6, respectively. The optimal diagnostic performance was obtained at the threshold of 0.5, with the precision and recall reaching 89% and 92%, respectively. Latent-stage diagnosis was also applied using background-subtracted spectra. The co-varying patterns of the 1 050 and 1 650 cm^-1 bands mirrored the symptomatic stages. The R_1650/1050 threshold of 0.5 was then achieved with 80% accuracy and an F1-score of 0.81, indicating the robust diagnostic efficacy for early infection detection. The high accuracy was combined with the interpretability using physiochemical biomarkers, particularly for early rice blast diagnosis. Unlike opaque machine learning, the R_1650/1050 ratio was directly linked to the spectral changes in the pathogen activity and structural degradation. The simple configuration and high speed (≤1 min per sample) can offer actionable insights for the field deployment in precision agriculture. Multi-spectral indicators can also be integrated to validate the model's robustness and scalability under diverse environmental conditions.