One of the most important conditions for the efficient operation of solar power plants with a large installed capacity is to ensure the systematic monitoring of the surface condition of the photovoltaic modules. This procedure is aimed at the timely detection of external damage to the modules, as well as their partial shading. The implementation of these measures solely through visual inspection by the maintenance personnel of the power plant requires significant labor intensity due to the large areas of the generation fields and the operating conditions. Authors propose an approach aimed at increasing the energy efficiency of high-power solar power plants by automating the inspection procedures of the surfaces of photovoltaic modules. The solution is based on the use of an unmanned aerial vehicle with a payload capable of video and geospatial data recording. To perform the procedures for detecting problem modules, it is proposed to use “object-detection” technology, which uses neural network classification methods characterized by high adaptability to various image parameters. The results of testing the technology showed that the use of a neural network based on the R-CNN architecture with the learning algorithm—Inception v2 (COCO)—allows detecting problematic photovoltaic modules with an accuracy of more than 95% on a clear day.

A solution for automated monitoring and diagnostics of photovoltaic modules of industrial solar power plants is proposed. The solution is based on the use of an unmanned aerial vehicle with a specialized payload and a ground-based intelligent information and control system to detect problem areas of the station, in particular partial shading and pollution. To perform the detection procedures, a neural network based on the Fast R-CNN architecture with the learning algorithm – Inception v2 (COCO) was used. The results of preliminary tests showed that the accuracy of detecting problem areas is at least 92%. The article presents a mathematical model that allows calculating the installed power monitored by the complex, depending on the type of station and UAV, meteorological parameters, and the performance of computing equipment. Numerical calculations have shown that when using the FIMI X8SE UAV and a computing device based on the RTX2080 GPU, the installed monitored power will be up to 7.5 MW.

Relevance. Proactive management of the processes of effective realization of the varietal potential of grapes is associated with the need to introduce innovative digital technologies for automated monitoring of heterogeneous data sources characterizing agro-climatic conditions and degradation processes of the biological state of plants. Currently, there is a steady trend aimed at digitalization of the viticulture and winemaking industry. There is a whole complex of scientific, practical, technical, technological tasks associated with the introduction of digital technologies for collecting the necessary information, aggregating it and creating a pre-processing technique for implementing procedures for multifactorial data analysis with their further use in decision support systems. The solution of the above-described tasks of a systemic nature requires the creation of scientific and methodological foundations for the implementation of intelligent adaptive automated monitoring of various objects and processes of agricultural enterprises.Methods. The above technology is based on the complex use of methods of technical vision, neural network classification and detection of grape leaves, evaluation of the quality of training neural network algorithms, video recording methods when using unmanned aerial vehicles (UAVs).Results. The results of the development of information technology for automated neural network detection of signs of deterioration of grape plantations for proactive management of the processes of effective realization of the varietal potential of grapes are presented. The technology allows the vineyard service personnel to promptly receive information about signs of deterioration of the condition of grape plantations based on video recording data of grape plants obtained using UAVs in static and dynamic mode. The results of testing the accuracy of detecting affected leaves showed that the mAP value of the trained neural network is at least 91%, which is sufficient to identify problem areas.

This study examines methods for improving air temperature measurement accuracy in agriculture using affordable meteorological equipment. Experimental results demonstrate that non-aspirated radiation shields introduce systematic measurement errors of up to 3 °C during daylight hours due to solar radiation heating. Two error mitigation approaches were evaluated: an aspirated shield system and a mathematical correction algorithm based on solar radiation intensity and wind speed measurements. The developed correction model, incorporating radiation shield geometry and heat transfer dynamics, reduced measurement deviations to 0.5–0.6 °C. The comparative analysis of data collected at Sevastopol meteorological station (June–August 2023) revealed a superior performance of the mathematical correction method (RMSE 0.17 °C, KGE 0.993) compared to the aspirated shield solution. The proposed correction algorithm achieves measurement accuracy within World Meteorological Organisation (WMO) standards without additional mechanical components that compromise system reliability. These findings facilitate the development of cost-effective distributed meteorological monitoring networks applicable to precision agriculture operations.

Relevance. Accurate air temperature measurement is a fundamental task in vineyard microclimate monitoring, as this parameter directly affects phenological processes, pathogen development, and the formation of grape quality characteristics. Available meteorological stations possess a significant limitation — substantial systematic errors when measuring temperature under conditions of intense solar radiation. The development of methods to minimize these errors is critically important for advancing precision viticulture technologies. Methods. The study is based on theoretical analysis and experimental validation of thermophysical processes in radiation shields. A specialized measurement setup was developed with meteorological systems: a ventilated shield with photovoltaic power supply and a non-ventilated shield, equipped with wind speed and solar radiation sensors. An original physical model of heat balance was proposed, accounting for convective heat exchange, radiative effects, and geometric characteristics of protective devices. An adaptive iterative algorithm was developed for real-time temperature correction calculations on microcontroller systems with mechanisms for handling singular states and ensuring stable convergence. Results. It was experimentally confirmed that non-ventilated shields demonstrate systematic errors up to 3 °C under high solar radiation. Ventilated shields reduce maximum deviations to 1 °C but require regular maintenance. The developed mathematical correction algorithm outperforms both alternative solutions: maximum deviations do not exceed 0.6 °C, mean deviations are 0.2 °C, normalized root-mean-square error is 3.5%, and the Kling — Gupta criterion reaches 0.993. The proposed solution provides an optimal balance of accuracy, costeffectiveness, and reliability for creating affordable dense networks for vineyard microclimate monitoring.