<br>Bee Tracker——一款基于机器学习的开源视频分析软件，用于评估空巢独居蜜蜂的筑巢和觅食性能,Ecology and Evolution

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Bee Tracker——一款基于机器学习的开源视频分析软件，用于评估空巢独居蜜蜂的筑巢和觅食性能
Ecology and Evolution ( IF 2.3 ) Pub Date : 2022-03-07 , DOI: 10.1002/ece3.8575
Anina C Knauer ₁ , Johannes Gallmann ₂ , Matthias Albrecht ₁

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蜜蜂的觅食和筑巢表现可以提供有关蜜蜂健康的重要信息，并且对于环境压力源的风险和影响评估具有重要意义。虽然射频识别（RFID）技术是一种有效的工具，越来越多地用于收集蜜蜂等社会蜜蜂物种的行为数据，但对独居蜜蜂的行为研究仍然在很大程度上依赖于直接观察，这非常耗时。在这里，我们提出了一种新颖的自动化方法，可以单独并同时跟踪和分析大量空巢独居蜜蜂的觅食和筑巢行为。该方法包括通过视频录制来监控嵌套单元，并通过基于机器学习的软件对视频进行自动分析。该Bee Tracker软件由四个训练有素的深度学习网络组成，用于检测进入或离开巢穴的蜜蜂，并根据蜜蜂在筑巢单元中的位置识别蜜蜂胸部的个体 ID 以及巢穴的 ID。该软件能够识别每只筑巢蜜蜂的每个巢穴，从而可以测量基于个体的繁殖成功率。此外，该软件还量化了雌性进入巢穴的数量，直到它找到自己的巢穴，作为巢穴识别的代理，并提供有关觅食旅行的次数和持续时间的信息。通过使用 8 个视频（每个视频记录 24 个筑巢雌性）对软件进行训练，该软件对这些参数的测量准确率达到了 96%。通过根据一组视频进行训练，该软件可以适应各种实验设置。所提出的方法可以有效地收集有关空巢独居蜜蜂物种的大量数据，并代表了一种有前途的新工具，用于在实验室、半野外和野外条件下监测和评估行为和繁殖成功率。

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Bee Tracker—an open-source machine learning-based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees

The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by machine learning-based software. This Bee Tracker software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees’ thorax and the IDs of their nests according to their positions in the nesting unit. The software is able to identify each nest of each individual nesting bee, which permits to measure individual-based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters. The software could be adapted to various experimental setups by training it according to a set of videos. The presented method allows to efficiently collect large amounts of data on cavity-nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi-field, and field conditions.

更新日期：2022-03-07

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