Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (<2%), indicating the universality of spectral patterns of stem rust. Experiments were conducted under controlled laboratory conditions; therefore, direct field transferability may be limited. These findings demonstrate that hyperspectral imaging with robust preprocessing and feature engineering enables early diagnostics of rust diseases in cereal crops.

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.