Hyperspectral Imaging for Early Detection and Severity Grading of Potato Bacterial Wilt

Potato (Solanum tuberosum) is a vital global non-cereal food crop severely threatened by bacterial wilt, caused by Ralstonia solanacearum(R. solanacearum). Conventional diagnostics like PCR and ELISA, though effective, are destructive and time-consuming, limiting large-scale field applications. This study investigates hyperspectral imaging (HSI) as a non-invasive, rapid, and accurate alternative for early detection and severity grading of potato bacterial wilt. Using a portable HSI system (400–1000 nm), spectral data were collected from inoculated potato plants (‘Longshu No. 7’) at 0, 24, 48, and 72 h post-inoculation, alongside disease severity assessment (grades 0–4). After comprehensive spectral preprocessing and feature band extraction via Competitivse Adaptive Reweighted Sampling (CARS), we developed two distinct sets of models: one for early detection (temporal classification) using Partial Least Squares-Discriminant Analysis (PLS-DA) and Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), and another for severity grading. The SNV + SG + MC + PLS-DA model achieved exceptional accuracy, exceeding 97% for early detection, while the MSC + SG + MC + CARS + PLS-DA model yielded >97% accuracy for severity grading. These results were supported by low misclassification rates in confusion matrices. This work establishes a robust HSI-based framework for high-throughput screening of resistant potato germplasm and advances precision agriculture strategies for bacterial wilt management.