Astuti Putri Kusuma, Hegedűs Bettina, Oleksa Andrzej, Bagi Zoltán, Kusza Szilvia
Centre for Agricultural Genomics and Biotechnology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary.
Doctoral School of Animal Science, University of Debrecen, 4032 Debrecen, Hungary.
Insects. 2024 Jun 4;15(6):418. doi: 10.3390/insects15060418.
Honeybees ( L.) are important for agriculture and ecosystems; however, they are threatened by the changing climate. In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilization of advanced machine learning techniques proves to be an exceptional tool. This review provides a comprehensive analysis of the available research on the different applications of artificial intelligence (AI) in beekeeping that are relevant to climate change. Presented studies have shown that AI can be used in various scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make decisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, and climate and habitat management, were analyzed. It was found that several environmental, behavioral, and physical attributes needed to be monitored in real-time to be able to understand and fully predict the state of the hives. Finally, it could be concluded that even if there is not yet a full-scale monitoring method for apiculture, the already available approaches (even with their identified shortcomings) can help maintain sustainability in the changing apiculture.
蜜蜂（Apis mellifera L.）对农业和生态系统至关重要；然而，它们正受到气候变化的威胁。为了适应并应对新出现的困难，养蜂人需要具备持续监测蜂箱的能力。为此，先进机器学习技术的应用被证明是一个出色的工具。本综述全面分析了人工智能（AI）在与气候变化相关的养蜂不同应用方面的现有研究。已发表的研究表明，AI可用于养蜂的各个科学领域，并能处理多种数据类型（如声音、传感器读数、图像），以在养蜂场进行调查、建模、预测并辅助决策。分析了与养蜂各个方面相关的研究文章，如蜂箱管理、保持蜂群健康、检测病虫害以及气候和栖息地管理等。研究发现，需要实时监测若干环境、行为和物理属性，以便能够理解并全面预测蜂箱状态。最后，可以得出结论，即使目前还没有针对养蜂的全面监测方法，但现有的方法（即使存在已识别的缺点）也有助于在不断变化的养蜂业中维持可持续性。
