Early Differentiation of Barley Diseases (Net Blotch, Spot Blotch, and Stem Rust) Using Hyperspectral Imaging and Machine Learning

This is the authors' abstract. We don't add key points for this paper.

Early and accurate detection of fungal diseases is critical for sustainable barley production, yet multiclass differentiation at early stages remains a major challenge. This study evaluated whether hyperspectral imaging (450–874 nm) combined with machine learning could distinguish three barley pathogens—Puccinia graminis f. sp. tritici (stem rust), Pyrenophora teres (net blotch), and Bipolaris sorokiniana (spot blotch)—during the early phase. We used a systematic ablative approach, comparing three classifiers (Logistic Regression, PLS-DA, and kNN) and several spectral feature representations across eight experimental splits to assess cross-experimental generalization. The best configuration, PLS-DA with spline-based features, achieved a mean balanced accuracy of 0.803 across the eight test experiments, with a standard deviation of 0.076. Classification performance increased steadily over the observation period, reaching a plateau above 0.900 from the third imaging day onward. Stem rust was consistently more difficult to classify than both blotch diseases; the reason for this remains unclear, although a longer latent period is a plausible factor. The results demonstrate that multiclass spectral differentiation of these three barley diseases is feasible at early stages under rigorous cross-experimental validation.