Abstract. Integration of machine learning systems into information systems for recognizing objects using unmanned aerial vehicles (UAVs) can be applied in many areas of life, including farming, military operations, and environmental monitoring. The purpose of this work is to develop an information system with the integration of machine learning for recognizing objects using UAVs in order to facilitate human labor and promote environmental protection by identifying various types of waste. This system is in demand because drones with cameras collect large volumes of data, and with the help of complex functions, their processing occurs faster. The work also opens up new opportunities for our country, due to many new functions and innovative solutions. Using the analysis of existing systems and functions, requirements for a new system were identified. Such as image recognition techniques, navigation of unmanned aerial vehicles, neural networks in the detection and distribution of objects, algorithms for planning the trajectory of the formation of UAVs. The tasks are to develop a system combined with deep learning technologies and using unmanned aerial vehicles. The system is equipped with an instant notification mechanism that responds to the detection of new objects and sends notifications to the user. A user-friendly interface is provided for control, receiving notifications, as well as storing images and data on previously recognized objects. The data obtained during the flight is transmitted to the information system for processing, recognition and classification of objects. The architecture of the solution ensures operation in real time. The user interface makes the system management intuitive and ensures long-term storage of information. As a result of the work, it can be used in various areas, such as security, finding the largest place of accumulation of waste, monitoring the environment. After the results were obtained, testing was carried out, which confirmed the accuracy of recognition and adaptability in real time.

Keywords: machine learning, unmanned aerial vehicles, object recognition, neural networks, deep learning, real-time processing, computer vision, data transmission.