The article is devoted to the study of spring wheat varieties cultivated in the steppe area of North-Eastern Kazakhstan and their resistance to the main pests. The dynamics of the number and phenology of spring wheat phytophages were investigated in relation to the varietal and climatic characteristics of agricultural production. Modern spring wheat varieties' resistance to the main pests was evaluated in the area of study for the first time. In the course of studying the harmfulness of pest complexes for the studied varieties, three-point mobile scales were used for the first time. They made it possible to identify patterns of varieties damage according to different pest numbers. Reliable differences of nine varieties with respect to the uptake of spring wheat by major pests, as well as their harmfulness, were established. 'Triso', 'Likamero', and 'Kurier' varieties with group resistance to them were identified. An economic evaluation of resistant spring wheat varieties was provided. The study used a three-point evaluation method with moving score limits to identify spring wheat varieties that were settled and damaged by pests. It enables the estimation of varieties with different background densities of insects and the comparison of data from different soil-climatic zones. The analysis of the obtained results confirms the high economic efficiency of pest-resistant varieties cultivation and the role of these varieties in the phytosanitary stabilization of agrobiocenoses and environmental protection. The results of the research could be used in the zoning of spring wheat varieties. Identified cultivars with group-based pest resistance might be used as donors for new breeding varieties.

This article presents the results of a comprehensive study aimed at developing automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI). The research aimed to create an effective plant disease detection system, including at the early stages, which is critically important for ensuring food security in regions where wheat plays a key role in the agro-industrial sector. The study analyses the spectral characteristics of major wheat diseases, including powdery mildew, fusarium head blight, septoria glume blotch, root rots, various types of leaf spots, brown rust, and loose smut. Healthy plants differ from diseased ones in that they show a mostly uniform tone without distinct spots or patches on hyperspectral images, and their spectra have a consistent shape without sharp fluctuations. In contrast, disease spectra, differ sharply from those of healthy areas and can take diverse forms. Wheat diseases with a light coating (powdery mildew, fusarium head blight) exhibit high reflectance; chlorosis in the early stages of diseases (rust, leaf spot, septoria leaf blotch) exhibits curves with medium reflectance, and diseases with dark colouration (loose smut, root rot) have low reflectance values. These differences in reflectance among fungal diseases are caused by pigments produced by the pathogens, which either strongly absorb light or reflect most of it. The presence or absence of pigment production is determined by adaptive mechanisms. Based on these patterns in the spectral characteristics and optical properties of the diseases, a classification model was developed with 94% overall accuracy. Random Forest proved to be the most effective method for the automated detection of wheat phytopathogens using hyperspectral data. The practical significance of this research lies in the potential integration of the developed phytopathology detection approach into precision agriculture systems and the use of UAV platforms, enabling rapid large-scale crop monitoring for the timely detection. The study’s results confirm the promising potential of combining hyperspectral technologies and machine learning methods for monitoring the phytosanitary condition of crops. Our findings contribute to the advancement of digital agriculture and are particularly valuable for the agro-industrial sector of Central Asia, where adopting precision farming technologies is a strategic priority given the climatic risks and export-oriented nature of grain production.

The spectral characteristics of harmful insect pests in wheat fields were characterised using hyperspectral imaging for the first time. The analysis of spectral profiles revealed that reflectance is determined by the structure of the insect’s chitin and the colouration of its body surface. Insects with lighter or more vivid colours (white, yellow, or green) showed higher reflectance values compared to those with predominantly dark pigmentation. Reflectance was also influenced by the presence of wings, surface roughness, and the age of the insect. Each species exhibited distinct spectral patterns that allowed for differentiation not only from other insect species but also from the plant background. A classification model using PLS-DA was developed and demonstrated high accuracy in identifying 12 pest species, confirming the strong potential of hyperspectral imaging for species-level classification. The results validate the PLS-DA method for differentiating insects based on spectral characteristics and underscore the reliability of this approach for automated monitoring systems to detect phytophagous pests in crop fields. This technology could reduce insecticide use by 30–40% through targeted application. The research has both scientific and economic significance, laying the groundwork for integrating machine learning and computer vision into agricultural monitoring. It supports the advancement of precision farming and contributes to improved global food security.

This study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections. We examined the causal agents of widespread crop diseases, including: loose smut, head blight, fusarium head blight (FHB), stem rust, net blotch, spot blotch, common root rot. Analysing disease-specific spectral characteristics with machine learning (ML) algorithms revealed the most informative spectral ranges: the green region (~520–560 nm), the red chlorophyll absorption zone (~650–680 nm), and the red-edge region (~700 nm). These ranges accurately reflect alterations in the plant’s cellular structure and pigment complexes. Spectral data were processed using five ML algorithms. Random Forest (RF) proved to be the most effective for identifying and differentiating barley diseases, achieving an accuracy of up to 90.13% (MCC = 0.86). This superior performance stems from the ensemble method’s robustness to noise and its ability to extract critical features from high-dimensional hyperspectral data, particularly when distinguishing diseases with overlapping spectral signatures. Furthermore, this study highlights the potential of integrating UAV-based remote sensing to delineate reference zones, proximal hyperspectral imaging (HSI), and ML for robust plant health monitoring. This combined approach shows significant promise for early disease diagnostics, enabling site-specific treatments, curbing disease progression, and reducing pesticide application. Ultimately, these findings offer practical value for the agro-industrial sector in major grain-producing countries, especially in Central Asia, where agricultural advancement is a strategic priority for sustainable development and food security.

Reliable identification of weed species is essential for effective and sustainable weed management. In this study, we explored the use of hyperspectral imaging to distinguish nine weed species based on their spectral signatures. Although the species showed similarities in their spectral curves due to comparable growing conditions, clear differences emerged related to morphological traits and pigment composition. We analysed the spectral data using five classification algorithms: Random Forest, Support Vector Machine, Artificial Neural Network, Maximum Entropy, and SIMCA. Model performance was assessed using per-class and overall accuracy. Random Forest outperformed the other methods, achieving 93.5% accuracy despite limited and imbalanced training data. This work contributes to the development of a spectral library for weed species and demonstrates the value of machine learning for species identification across different crops and environmental conditions. Expanding such spectral databases can enhance the speed and accuracy of weed monitoring, reduce herbicide reliance, and reduce environmental impact. The proposed approach shows strong potential for integration into precision agriculture and agroecological monitoring systems, supporting more efficient and environmentally responsible farmland management.

This article is dedicated to studying the influence of ash element distribution in the tissues of woody plants` foliar organs on the fluctuating asymmetry of leaf blades in industrial areas. The research objects are leaves of Populus balsamifera, Betula pendula, Acer negundo. The leaves were collected at the end of the 2023 growing season, with subsequent measurements of the length and width of the leaf blade and calculation of the standard deviation. The significance of differences between samples was assessed based on the results of variance analysis. Ash content determination was carried out by the dry ashing method of leaves in a muffle furnace. The obtained results confirm a direct proportional relationship between significant fluctuations in morphometric parameters as ash content increases, which can be used as an indicative method of anthropogenic pollution in industrial regions.