# Deep Learning Seehive Monitoring System for Early Detection of the Varroa Mite
###### 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.  
beehive monitoring systems; Varroa mite detection; Internet of Things (IoT); Convolutional Neural Networks (CNNs); image processing; performance evaluation; distributed systems +
Footnote †: journal: _Acticle_  
Significant improvements have been made toward monitoring beehive conditions using cameras over the last few years. Such systems try to replace systems that use IR sensors at the beehive entrance or sound sensors [13, 19, 20, 21] and translate sensory information to conditions status. Therefore, image processing techniques and deep learning techniques have been utilized to monitor bees, and their actions at the hive entrances, as mentioned at [22, 23]. Image processing methods are the easiest to implement. However, deep learning methods produce the most reliable condition outcome results, such as swarming or external attacks. Nevertheless, if such probing methods are performed only at the beehive entrance, they cannot reliably detect the occurring event early enough for the beekeepers to ameliorate them successfully.  
This paper investigates existing technological systems focusing on implementing an IoT system for detecting Varroa mite in bees, which leads to major Colony Collapse Disorders (CCD) around the globe. Since Varroa infestation events occur mainly in the late spring (Apr-May) and first Autumn months, as indicated by the apiarists, this paper presents a new camera sensor system for detecting Varroa inside the beehive. The proposed system uses a camera that incorporates an image processing motion logic and utilizes a Convolutional Neural Network CNN pre-trained model for detecting bees. The rest of this paper is structured as follows: Section 2 presents related work in existing technological products for Varroa mite identification and confrontation. Section 3 presents the authors' proposed Varroa detection system and system capabilities. Section 4 presents the proposed CNN algorithm used by the system. Section 5 presents the authors' system experimentation (offline and online), evaluation, and cross-comparison with existing literature systems. Finally, Section 6 concludes the paper.