| 孙逸飞，丁桂玲，路运才，刘振虎，黄家兴,2023,深度学习在蜜蜂研究中的应用[J].环境昆虫学报,45(5):1150-1160 |
| 深度学习在蜜蜂研究中的应用 |
| Application of deep learning in honeybee researches |
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| DOI： |
| 中文关键词: 深度学习 目标检测 行为跟踪 蜂群健康 蜂巢监测 |
| 英文关键词:Deep learning object detection behavioral tracking colony health bee hive monitoring |
| 基金项目:中国农业科学院科技创新工程(CAAS-ASTIP-2023-IAR)；国家蜂产业技术体系（CARS-44-KXJ5） |
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| 中文摘要: |
| 实现对蜜蜂蜂群的实时动态监测，有助于养蜂业的数字化与智能化发展，对大幅提升养蜂管理水平具有重要意义。深度学习作为人工智能的一种新的研究方向，目前已被广泛应用于昆虫分类学、行为学、害虫生物防治等领域。随着深度学习检测算法的迅速发展，基于深度学习的蜜蜂蜂群监测技术不断涌现，为智能化养蜂提供了可能。为促进深度学习在蜜蜂领域的进一步应用，本文梳理了深度学习在蜜蜂的物种识别、行为跟踪监测、蜂群健康监测和蜂巢监测等方面的研究进展，分析了深度学习技术在蜜蜂蜂群监测研究及应用中存在的一些问题和未来发展方向，为深度学习在蜜蜂领域的应用提出了建议。 |
| 英文摘要: |
| The growth of intelligent apiculture and the improvement of apiculture management depend significantly on the real-time monitoring of honeybee colonies. Deep learning, a new research direction of artificial intelligence, has been widely applied in insect taxology, insect ethology and pest bio-control. With the rapid development of deep learning-based detection algorithms, deep learning-based bee colony identification technologies are increasingly published, opening the door to intelligent beekeeping. This study reviews the advancement of deep learning in bee species identification, behavioral tracking and monitoring, colony health monitoring, and beehive monitoring, among other applications, to further enhance the application of deep learning in bees. Additionally, we analyze the problems and future direction of deep learning technology in honeybee colony detection research and application and offer a basic foundation for additional study. |
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