The object of this study is an autonomous Raspberry Pi-based device for real-time pest detection. The task addressed relates to the lack of affordable, energy-efficient, and autonomous solutions for working in the field without an Internet connection. The paper reports the design of an intelligent device for pest monitoring. The device is focused on automatic recognition of the striped grain flea beetle (Phyllotreta vittula) in grain crops. As a result of the study, a system was designed based on the Raspberry Pi 4.0 microcomputer using the OpenCV library and the YOLO model. The device processes the video stream, identifies pests, and saves data locally. The system provides high accuracy at low power consumption. This was made possible by a lightweight neural network architecture and optimized image processing. A distinctive feature of the solution is autonomy, mobility, and resistance to variable lighting conditions. The system also works with limited computing resources. The results demonstrate that the device could be effectively used in precision farming systems and at scientific institutions. The device helps identify pests and make agricultural decisions at early stages of infection. The technological advancement could be adapted to other types of pests with minimal changes to the model. In the future, the system could be integrated into broader agricultural monitoring platforms with the ability to transfer data to the cloud. The practical use of the device is possible both in large farms and on private farms. This technological advancement is especially relevant for regions with limited technical infrastructure