Deep Learning Beehive Monitoring System for Early Detection of the Varroa Mite

Signals Pub Date : 2022-07-28 DOI:10.3390/signals3030030
George Voudiotis, Anna Moraiti, Sotirios Kontogiannis
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引用次数: 7

Abstract

One of the most critical causes of colony collapse disorder in beekeeping is caused by the Varroa mite. This paper presents an embedded camera module supported by a deep learning algorithm for the process of early detecting of Varroa infestations. This is achieved using a deep learning algorithm that tries to identify bees inside the brood frames carrying the mite in real-time. The end-node device camera module is placed inside the brood box. It is equipped with offline detection in remote areas of limited network coverage or online imagery data transmission and mite detection over the cloud. The proposed deep learning algorithm uses a deep learning network for bee object detection and an image processing step to identify the mite on the previously detected objects. Finally, the authors present their proof of concept experimentation of their approach that can offer a total bee and varroa detection accuracy of close to 70%. The authors present in detail and discuss their experimental results.
用于早期检测瓦螨的深度学习蜂巢监测系统
蜂群衰竭失调的最重要原因之一是由瓦螨引起的。本文提出了一种基于深度学习算法的嵌入式摄像机模块,用于Varroa侵扰的早期检测过程。这是通过一种深度学习算法实现的,该算法试图实时识别携带螨虫的窝框内的蜜蜂。端节点设备摄像模块放置在育雏箱内。具备网络覆盖范围有限的偏远地区离线检测或在线图像数据传输和云端螨虫检测功能。提出的深度学习算法使用深度学习网络进行蜜蜂目标检测,并使用图像处理步骤识别先前检测到的物体上的螨虫。最后,作者展示了他们的方法的概念实验证明,该方法可以提供接近70%的蜜蜂和瓦罗亚检测精度。作者详细介绍并讨论了他们的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.20
自引率
0.00%
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审稿时长
11 weeks
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