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.

The growing importance of university rankings in higher education requires evaluation methods that are both predictive and interpretable. Traditional ranking approaches are usually based on fixed weighted indicators, while purely data-driven machine learning models often provide limited transparency for institutional decision-making. This paper proposes a hybrid analytical–machine learning framework for evaluating and forecasting university rankings using multidimensional performance indicators. The framework combines an interpretable weighted aggregation model, an XGBoost-based residual correction mechanism, an LSTM forecasting module, and SHAP-based explainability analysis. The empirical dataset was constructed as a university-year panel covering 20 Kazakhstani universities over the 2019–2024 period, resulting in 120 observations. The input indicators included publication output, citation impact, h-index, international collaboration, and research funding. The models were evaluated using a temporal validation protocol, with 2019–2022 used for training, 2023 for validation, and 2024 as the independent test subset. The results show that the hybrid model reduced RMSE from 0.185 to 0.092 compared with the analytical model and from 0.124 to 0.092 compared with the standalone XGBoost model. Similar improvements were observed for MAE, MAPE, and ranking error. However, the remaining deviations indicate that the model should be interpreted as reducing, rather than eliminating, prediction uncertainty. SHAP analysis showed that citation impact and publication output had the widest feature-contribution distributions, while all indicators demonstrated both positive and negative effects depending on the observation. The proposed framework can support institutional analytics by combining ranking evaluation, forecasting, and interpretable identification of key performance drivers.

Vision-based perception is essential for robotic manipulator grasping because the accuracy of object localization directly affects grasp planning and motion execution. This paper presents a comparative analysis of computer vision algorithms for object detection and localization in a structured robotic pick-and-place scenario. Classical edge detection methods, including Canny, Sobel, Prewitt, and Roberts, were applied to the same RGB image of a manipulator workspace containing box-shaped objects. The algorithms were evaluated using detection accuracy, F1-score, centroid localization error, contour stability, and processing time. The results showed that Canny provided the most reliable performance, detecting all 12 target objects with the highest localization stability. The study confirms that classical edge detection can support lightweight vision-based robotic grasping.

Precision farming methods require consideration of subtle differences in plant growth processes in different areas of cultivated arable land. Differences in the relief of the field, its water supply, the thickness of the humus layer, etc. cause the need to rank the arable land for the application of agrotechnical operations of different intensities, which ultimately leads to higher yields and lower costs of agricultural crops. The ranking of arable land within a field is usually accomplished by segmentation of remotely sensed data. The creation of a segmentation system requires periodic remote and ground monitoring of fields, collection and processing of the received information with its geographical reference. Both satellite remote sensing systems and unmanned aerial platforms (UAPs) can be used for this purpose. The volume of information received, especially when using UAVs, is very significant, and the requirements for the speed of processing are high. In this regard, efficient methods of systematization and processing of the received images, which rely on sufficiently fast segmentation algorithms, are relevant. This paper considers methods of image segmentation from various remote sensing systems, which allow to increase the economic performance of agronomic measures in the precision farming loop. An example of a threshold segmentation program is given, which can be used separately or as part of an information system to support precision farming processes. The paper presents the results of its application for segmentation of satellite images of a field by NDVI index value. The conducted analysis and recommendations on the segmentation data will contribute to the prevention of environmental violations, yield losses due to sudden changes in weather conditions and differences in the relief of cultivated arable land.