Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods

作者：Weinan Li, Lisen Liu, Jianing Li, Weiguang Yang, Yang Guo, Longyu Huang, Zhaoen Yang, Jun Peng, Xiuliang Jin, Yubin Lan · 发表于：Frontiers in Plant Science · 年份：2025 · DOI：10.3389/fpls.2025.1519001 · 被引用次数：9 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Spectroscopy and Chemometric Analyses

Introduction: Verticillium wilt is a severe soil-borne disease that affects cotton growth and yield. Traditional monitoring methods, which rely on manual investigation, are inefficient and impractical for large-scale applications. This study introduces a novel approach combining machine learning with feature selection to identify sensitive spectral features for accurate and efficient detection of cotton Verticillium wilt. Methods: We conducted comprehensive hyperspectral measurements using handheld devices (350-2500 nm) to analyze cotton leaves in a controlled greenhouse environment and employed Unmanned Aerial Vehicle (UAV) hyperspectral imaging (400-995 nm) to capture canopy-level data in field conditions. The hyperspectral data were pre-processed to extract wavelet coefficients and spectral indices (SIs), enabling the derivation of disease-specific spectral features (DSSFs) through advanced feature selection techniques. Using these DSSFs, we developed detection models to assess both the incidence and severity of leaf damage by Verticillium wilt at the leaf scale and the incidence at the canopy scale. Initial analysis identified critical spectral reflectance bands, wavelet coefficients, and SIs that exhibited dynamic responses as the disease progressed. Results: Model validation demonstrated that the incidence detection models at the leaf scale achieved a peak classification accuracy of 85.83%, which is about 10% higher than traditional methods without feature selection. Th...