Non‐Destructive Testing Based on Hyperspectral Imaging for Determination of Available Silicon and Moisture Contents in Ginseng Soils of Different Origins

ABSTRACT Soil‐available silicon (SAS) and soil moisture (SM) contents are critical parameters for crop growth; however, traditional detection methods are time‐consuming and inefficient. This study aimed to develop a non‐destructive testing method using hyperspectral imaging (HSI) technology for the rapid and real‐time detection of SAS and SM in ginseng soils of various origins. Twenty‐two batches of soil samples and 51 batches of ginseng samples were collected, and spectral data in the visible near‐infrared (VNIR) and shortwave infrared (SWIR) ranges were acquired simultaneously using an HSI system. To reduce data redundancy, principal component analysis for variable dimensionality reduction and a genetic algorithm (GA) involving iterative and voting methods were employed to process spectral data. The results showed that for SAS, the raw ELM performed best (SWIR R v 2 = 0.88, RMSE = 28.19), while BP‐GA3 peaked after GA (SWIR Rv 2 = 0.93, RMSE = 15.47). For SM, the raw BP (SWIR R v 2 = 0.89, RMSE = 3.16), BP‐GA3 achieved the highest GA result (SWIR Rv 2 = 0.94, RMSE = 1.80). PCA consistently underperforms (lowest SAS PCA‐ELM SWIR R v 2 = 0.41). Combined PCA and SAM analysis revealed distinct ginseng classification by origin, with RF achieving 77.78% (test) and 100% (train) accuracy for soil in SWIR, while BP model yielded 73.33% (test) and 80.56% (train) accuracy for ginseng in VNIR, demonstrating effective differentiation. This study provides theoretical support and a practical basis for the non‐destructive testing of ginseng soil from the three provinces of Northeast China based on hyperspectral imaging; however, further expansion of the studied research samples is required to verify the generalization ability of the developed model.