Advanced hyperspectral image processing and machine learning approaches for early detection of wheat stem rust

Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust (<i>Puccinia graminis</i> f. sp. <i>tritici</i> Eriks. and E. Henn.) of wheat (<i>Triticum aestivum</i> L.). The pipeline implementation enhanced the classification models accuracy raising F1-scores of logistic regression, support vector machines and Light Gradient Boosting Machine from 0.67-0.75 (raw spectra) to 0.86-0.94. Notably, it enabled reliable detection of asymptomatic infections as early as 4 days after inoculation, which was not achievable without preprocessing. The framework demonstrates potential for generalization beyond plant pathology, suggesting applicability to a range of hyperspectral remote sensing tasks such as vegetative health monitoring, environmental assessment, and material classification through improved signal interpretability and robustness. This work lays the groundwork for advancing hyperspectral image processing by proposing a reproducible, scalable pipeline that could be adapted for integration into unmanned and satellite imaging systems.