The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

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.

A new method called the Survival Beran-based Neural Importance Model (SurvBeNIM) is proposed. It aims to explain predictions of machine learning survival models, which are in the form of survival or cumulative hazard functions. The main idea behind SurvBeNIM is to extend the Beran estimator by incorporating the importance functions into its kernels and by implementing these importance functions as a set of neural networks which are jointly trained in an end-to-end manner. Two strategies of using and training the whole neural network implementing SurvBeNIM are proposed. The first one explains a single instance, and the neural network is trained for each explained instance. According to the second strategy, the neural network only learns once on all instances from the dataset and on all generated instances. Then the neural network is used to explain any instance in a dataset domain. Various numerical experiments compare the method with different existing explanation methods. A code implementing the proposed method is publicly available.