This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

Urban air pollution caused by fine particulate matter with an aerodynamic diameter of less than 2.5 micrometers (PM2.5) remains a major environmental and public health challenge, especially in metropolitan areas with spatially heterogeneous pollutant distribution. Purpose. The study aims to develop and evaluate a computationally efficient short-term forecasting model for predicting PM2.5 concentrations in an urban agglomeration while accounting for both temporal dynamics and spatial relationships between monitoring stations. Objectives. The objectives include preprocessing and standardization of sensor data, construction of local spatio-temporal input subsets for each target station using data from the three nearest neighboring sensors, integration of inter-sensor distances as spatial features, and assessment of predictive performance. Methods. The dataset comprises hourly PM2.5 measurements from 32 stationary sensors collected over an 11-month period. Preprocessing involved anomaly removal, temporal alignment to a uniform hourly grid, z-normalization, and formation of sliding windows of 10-time steps. Spatial context was incorporated using a distance matrix based on geographic coordinates. A multilayer perceptron neural network with fully connected layers, batch normalization, and dropout was trained to predict concentrations one hour ahead. Performance was evaluated on temporally separated training, validation, and test sets using the coefficient of determination, mean absolute error, root mean square error, mean absolute percentage error, and symmetric mean absolute percentage error. Results. The model demonstrated stable convergence and consistent generalization. The coefficient of determination reached 0.80 on validation data and 0.79 on the test set, with 0.81 on the full dataset. The correlation between observed and predicted values was 0.91. Conclusions. The findings indicate that a moderate-complexity multilayer perceptron with explicit spatial feature integration can provide reasonably accurate and computationally efficient short-term PM2.5 forecasts, representing a practical solution for urban air quality monitoring.

This study analyzes air pollution by fine particulate matter (PM2.5) in Almaty, the largest megacity of Kazakhstan. The city is located in a basin formed by the spurs of the Zailiyskiy Alatau range. Due to its geographical features, it is highly exposed to pollution by anthropogenic fine particulate matter. The main sources of air pollution include transport, industrial enterprises, and heating systems. The objectives of this study are to identify the dominant source of pollution, to assess spatial heterogeneity between central and foothill districts, and to evaluate the effectiveness of household air purifiers in improving indoor air quality. Urban vehicular traffic exhibits a periodic temporal pattern, with daily minima at night and weekly minima on weekends. This dynamic behavior enables a quantitative assessment of its contribution to the overall PM2.5 concentration in the urban atmosphere. The results suggest that heat generation systems are likely to be the dominant contributor to PM2.5 pollution during the autumn–winter period. In addition, observations indicate that high pollution levels in foothill areas occur approximately 30% less frequently than in central districts. The use of household air purification systems may reduce indoor particulate matter concentrations severalfold under the observed conditions.