Crop Disease Spore Detection Method Based on Au@Ag NRS

Yixue Zhang, Jili Guo, Fei Bian, Zhaowei Li, Chuandong Guo, Jialiang Zheng and Xiaodong Zhang ()
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Yixue Zhang: Basic Engineering Training Center, Jiangsu University, Zhenjiang 212013, China
Jili Guo: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Fei Bian: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhaowei Li: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Chuandong Guo: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Jialiang Zheng: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xiaodong Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 19, 1-22

Abstract: Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection.

Keywords: crop disease spores; fingerprint spectra; surface-enhanced Raman scattering (SERS); detection method (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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