Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, 14850, USA.
Department of Entomology, Cornell University, Ithaca, NY, 14850, USA.
Sci Rep. 2021 Feb 19;11(1):4239. doi: 10.1038/s41598-021-82537-1.
In digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems.
在数字农业中，大规模的数据采集和分析可以通过让种植者不断监测田地的状态来改善农场管理。然而，部署大规模的自主机器人团队来导航和监测杂乱的环境既困难又昂贵。在这里，我们提出了一些方法，可以利用配备微型飞行记录器的管理型蜜蜂群体来监测果园授粉活动。跟踪蜜蜂的飞行可以提供作物授粉的估计，从而使种植者能够提高产量和资源分配效率。蜜蜂擅长在复杂的环境中机动，并且通过数千次日常飞行来共同汇集关于花蜜和花粉源的信息。此外，对于许多作物来说，在开花前和开花期间，蜂群都会出现在果园中，因为种植者经常租用蜂箱以确保成功授粉。我们对现有的角度敏感像素 (ASP) 进行了特征描述，以便在飞行记录器中使用，并计算了内存和分辨率之间的权衡。我们进一步将 ASP 数据集成到一个蜂群觅食模拟器中，并展示了大量的飞行如何通过来自机器人测绘文献的方法来提高系统的准确性。我们的研究结果表明，这种农业监测具有很大的潜力，我们可以利用社会性昆虫感知物理世界的优势，同时提供与专门设计的系统相当的数据采集。
