The accumulation of salt in arable lands is a source of significant abiotic stress, contributing to a 10% decline in the world’s total arable lands and threatening food productivity and the sustainability of agriculture. About 76 million hectares of productive land are estimated to have been affected by human-induced salinization such as extreme salt deposits in soil, which are mainly caused by the actions of humans. For instance, continued irrigation and the frequent use of chemical fertilizers need to be understood. To ensure food availability, it is essential to improve upon traditional farming methods using current technologies to facilitate the reclamation of saline-affected arable lands to achieve high and sustainable food production. This review details current innovative strategies such as the modification of metabolic pathways, manipulation of antioxidant pathways, genetic engineering, RNA interference technology, engineered nanoparticles, arbuscular mycorrhizal fungi (AMF), organic amendments, and trace elements for improving saline marginal lands. These strategies were identified to have contributed to the improvement of plants salinity tolerance in diverse ways. For instance, the accumulation of plant metabolites such as amino acids, sugars, polyols, organic acids, saponins, anthocyanins, polyphenols, and tannins detoxify plants and play crucial roles in mitigating the detrimental effects of oxidative damage posed by salinity stress. Multiple plant miRNAs encoding the up- and down-regulation of single- and multi-ion transporters have been engineered in plant species to enhance salt tolerance. Nanomaterials and plant root system colonized by arbuscular mycorrhizal increase water uptake, photosynthetic efficiency, and biomass allocation in plants exposed to saline stress by excluding 65 percent of the Na⁺ uptake and enhancing K⁺ uptake by 84.21 percent. Organic amendments and trace elements reduced salinity concentrations by 22 percent and improved growth by up to 84 percent in maize subjected to salinity stress. This study also discusses how researchers can use these strategies to improve plants growth, development, and survival in saline soil conditions to enhance the productivity and sustainability of agriculture. The strategies discussed in this study have also proven to be promising approaches for developing salinity stress tolerance strategies for plants to increase agricultural productivity and sustainability.

Zeolite materials are among the relatively cheap and readily available materials for wastewater treatment. However, the performance of zeolite-based systems can be highly affected by the material properties. In this study, the treatment system based on natural zeolite materials from Chankanai mines in Kazakhstan was compared with a synthetic zeolite treatment system for the purification of groundwater. Water quality indices were also developed from a set of selected water quality parameters to further assess the state of water quality of raw groundwater and the effluents treated with natural and synthetic zeolite. The lowest removal efficiency of natural zeolite (30%) was observed with zinc, while the lowest removal efficiency (36%) of synthetic zeolite was observed with arsenic. With turbidity and beryllium, we observed the maximum removal efficiency (100%) of natural zeolite, whereas with turbidity, we observed the highest removal efficiency (100%) of synthetic zeolite. When the groundwater samples were put through the natural zeolite treatment system, removal efficiency of 50% and above was obtained with 27 (79.4%) out of the 34 water quality parameters examined. On the other hand, when the groundwater samples were put through the synthetic zeolite treatment system, more than 50% removal efficiency was attained with 30 (88.2%) out of the 34 water quality parameters studied. The aggregated water quality index of raw groundwater was 3278.24, falling in the “water unsuitable for drinking” category. The effluent treated with natural zeolite generated 144.82 as a water quality index, falling in the “poor water” quality category. Synthetic zeolite generated 94.79 as a water quality index, falling in the “good water” quality category. Across the board, it was shown that the synthetic zeolite treatment system outperformed the natural zeolite treatment system according to a number of water quality parameters. The findings of this study offer substantial knowledge that can be used to develop more efficient groundwater treatment technologies.

Major losses of fresh tomatoes happen during post-harvest storage due to prompt senescence and diseases. The aim of the research was to evaluate the effects of different spectra of LED lights on the post-harvest preservation of ascorbic acid, lycopene, and total soluble solids, the weight and size of tomato fruits, as well as to determine the optimal exposure time and distance of irradiation and extension of shelf-life. Therefore, experiments were carried out in a climate chamber with shelves equipped with three different light spectra: red light-emitting diodes, red–blue–white light-emitting diodes, and ultraviolet-light-emitting diodes. Light treatment had a certain positive effect on the firmness, size, and mass of samples. Thus, tomato fruits (Panekra) exposed to the spectra of LED lights demonstrated a better quality of firmness and mass compared to the control samples (non-preserved) of tomatoes. The treatments with RL significantly improved the concentration of lycopene than FL and UV-LED lights, although the highest concentration of lycopene was observed in the control samples for the first 7 days of the storage. After 21 days, the ascorbic acid content in the red spectrum was found to be much higher than in the other two spectra and control samples, coming in at about 1.8 mg/100 mL compared to 1.0 mg/100 mL for the control samples. Total soluble solids also increased significantly after preservation, rising from 3.9 °Brix in the control samples to roughly 7.3 °Brix in samples preserved using the full spectrum after 21 days. Overall, the results of the study demonstrated that tomato preservation using the investigated techniques induced lycopene concentration, ascorbic acid, and total soluble solids concentrations. The results derived from this study provide highly useful information in the field of post-harvest preservation.

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.

To improve plant resistance to pests, we analysed the impact of various agronomic practices on the number and species diversity of pests in the crops of spring wheat varieties of foreign and Kazakhstan breeding in North- Eastern Kazakhstan. The intensive development of agriculture, resulting in the new technological flow processes of wheat growing, the sowing of foreign varieties not previously cultivated under local conditions, and climate change contribute to the formation of new food chains in agrocenoses. These new food chains require the monitoring of plant-feeding species with the help of ecological approaches and techniques. Efficient protection of crops with plant feeders requires constant updating on the phytosanitary in agrocenoses. Information on phytosanitary monitoring previously carried out in the region is not available, so it became necessary to collect data and analyse the number and species composition of wheat pests, considering new foreign varieties and cultivation technology practices. The research was carried out in 2021 in typical agricultural organisations of the North-Eastern regions of Kazakhstan with different preceding crops. The vegetation period was characterised by high atmospheric temperatures and a lack of moisture in the soil in spring and summer, contributing to decreasing of plant turgor and damage resistance. Early sowing of the ‘Triso’ wheat variety was affected by high temperatures and lack of soil moisture in the initial stage of development, which delayed its growth and made it more susceptible to pest damage. The other varieties were sown in optimal dates recommended by regional scientific institutions.

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.