A Novel Spectral Vegetation Index for Improved Detection of Soybean Cyst Nematode (SCN) Infestation Using Hyperspectral Data

Soybean cyst nematode (SCN) is a pathogen with serious impacts on soybean yields, yet traditional field-based assessment is labor-intensive and often ineffective for early interventions, and the existing spectral vegetation indices (VIs) also lack the ability to accurately detect SCN infested plants. This study aimed to develop an improved detection method using hyperspectral data. A greenhouse-based experiment was designed to collect 100 hyperspectral datasets from 20 soybean plants inoculated with four SCN egg levels (0–10,000) from the 68th to 97th day after planting. Based on spectral similarity and inoculation levels, three stress classes were defined as proxies for actual plant stress: healthy (0 egg), moderate (1000 and 5000 eggs), and severe (10,000 eggs). These classifications are based on predefined inoculation thresholds and spectral trends, which may not fully align with direct physiological stress measurements due to inherent variability in individual plant responses. Through analysis of variance (ANOVA), principal component analysis (PCA), feature selection, and classification comparison, a new spectral VI, called SCNVI, was proposed using bands 338 nm and 665 nm. The SCNVI coupled with eXtreme Gradient Boosting (XGBoost) achieved an accurate classification of 70% for three classes and outperformed the 12 traditional VIs. These findings suggest that integrating the SCNVI and XGBoost algorithm provides the potential for improving the detection of SCN infestation, though further validation in field environments is required to confirm its practical applicability.