Early Detection of Soybean Mosaic Virus (SMV) Using Portable Raman Spectroscopy coupled with Machine Learning

Yujia Han¹Hongpu Guan²Dagang Wang³Yafei Zhang⁴Weixuan Zhang¹Yiming Zhao¹Longgang Zhao¹Zhaohua Wang⁵TINGTING WU²Yanru Zhao²Hexiang Luan^1*

• ¹Qingdao Agricultural University, Qingdao, China
• ²Northwest A&F University College of Mechanical and Electronic Engineering, Yangling, China
• ³Crop Institute of Anhui Academy of Agricultural Sciences, HEFEI, China
• ⁴Qingdao Agricultural University, Qingdao, China
• ⁵Institute of Agricultural Information and Economics ,Shandong Academy of Agricultural Sciences, Jinan, China

Soybean mosaic virus (SMV) is one of the major pathogens affecting global soybean yield and quality, and its early and accurate detection is essential for disease warning and precision management. This study proposes a non-invasive early detection method by integrating portable Raman spectroscopy with artificial intelligence algorithms. Raman spectra of leaves from both resistant and susceptible soybean cultivars were collected at different infection stages (0, 2, 4, and 6 days post-inoculation), and preprocessed using Savitzky–Golay (S-G) smoothing and adaptive iteratively reweighted penalized least squares (Air-PLS) baseline correction. Spectral feature analysis revealed significant changes in carotenoid levels caused by viral infection, and distinct spectral responses between resistant and susceptible cultivars during disease progression. Four classification models—1D-CNN, SVM, KNN, and BP-ANN—were developed to classify samples from different infection stages, among which the 1D-CNN model achieved the highest prediction accuracy of 90%. In addition, principal component analysis (PCA) indicated that the Raman spectroscopy-based method significantly advanced the early detection of SMV (SC3) to 4 days post-inoculation, compared to 7-10 days required by conventional methods. This evidences the superior capability of Raman spectroscopy for monitoring the dynamics of SMV infection and its potential to considerably reduce the duration of diagnosis. This study confirms the feasibility and efficiency of Raman spectroscopy combined with deep learning for in situ early detection of plant viral diseases and provides a promising reference for non-destructive diagnosis of early-stage foliar infections.