This paper presents a video recognition-based solution for an innovative beekeeping system, which we have developed and have evaluated in a prototype. We have designed this solution to improve the monitoring and management of beekeeping operations, a critical aspect for ensuring efficiency and sustainability in modern apiculture. The proposed system enables continuous monitoring of bee colonies, providing real-time insights into environmental changes and bee behavior. In addition, the system's analytical tools can identify and analyze correlations between following factors affecting bee productivity, such as weather conditions, geographical location of the apiary, and other environmental influences.The introduction of this stand-alone beekeeping solution offers beekeepers the ability to remotely track key hive conditions. For instance, it can detect whether the temperature inside the hive is nearing critical levels, or whether the bees are facing food shortages. This timely information allows beekeepers to take swift action to prevent potential harm to the colonies, thereby improving overall colony health and productivity. Such a system must be particularly beneficial during winter, helping to preserve hibernating bee colonies, while also saving valuable time and resources typically spent on manual hive inspections.The autonomous beekeeping system aligns with the broader goals of promoting economically sustainable agriculture, supporting the conservation of agricultural and forestry resources. The introduction of advanced monitoring and control features ensures that beekeepers can maintain optimal hive conditions with minimal manual intervention. Moreover, as the project progresses, we are planning further enhancements, such as incorporating artificial intelligence (AI) techniques, including neural networks, to enhance image processing capabilities.