# Deep Learning Seehive Monitoring System for Early Detection of the Varroa Mite
## 2 Related Work
### Related Work on Varroa Mite Detection Technologies  
A technological method reported by [29] uses the E-nose technology. Its use is to detect Varroa, when the infestation influences the chemical composition of the air inside a hive. The deciding factor for the effectiveness of this technique is the time of detection. Sound monitoring systems using MEL or FFT spectrograms are also powerful tools for Varroa mite indications. The use of frequency-amplitude over time representationscombined with SVMs and neural network classifiers can distinguish between different hive states, specifically external attacks, colony stress, swarming, and queen loss [19]. Detection of colony collapse can be exploited as an indicator of the existence of the Varroa mite. The authors at [13, 30, 31] also present their implementations of low power Wireless Sensor Network (WSN) technology, assisted by cloud computing, to monitor the existence of bees stress behavior, that also includes the existence of the Varroa mite. The key objective of this research is to use WSN technology to notice a beehive colony and collect key information about the activity/environment within a beehive and the health of the bees. Nevertheless, as mentioned by [13], Varroa mite detection using sensors (temperature, humidity, noise level, and gas sensors), may include many false detection cases, since other phenomena such as swarming, queen loss, or even hunger may lead to CCDs as the ones caused by the mite. Based on its eco-friendly and sustainable nature, accurate results, and futuristic design, Var-Gor [32] is a promising device for the early detection of Varroa mite as well as its early struggle. Specifically, when a contaminated bee with varroa mite enters an uncontaminated hive, the Var-Gor system detects the mite by image capturing, template matching, color classification, and segmentation filters. Moreover, this sends a warning by notification to the beeekeeper's phone. For real-time bee monitoring using cameras, including deep learning approaches, the authors at [33] developed an experimental system. Their proposition is built on a single-board computer Raspberry Pi (RPi) platform and aims to analyze video streams with bees and detect varroosis. They also applied two distinct detection processes with two CNN models, one for the bees and the other for the Varroa. Nevertheless, the camera is located outside the hive, making it hard for mite early detection. In case of infection, the pictures of the infected bees are transferred to the data center in the Cloud for further analysis, storing, upgrading their CNN models, and sending a notification to the relevant beeeeeper. The use of two distinct CNN models makes this approach hard to apply detection to a standalone RPi device inside the device, as the authors of this paper propose with their offline system approach. Furthermore, the use of image processing techniques such as edge detection Hough transformations, region labeling, and color masking can accurately identify the mite on the detected bees, provided by a CNN trained network that accurately detects bees, similar to the one the authors of this paper propose. The authors at [34] present a camera-based approach of CNN-trained networks using the classification of infected and non-infected bees manually and then utilizing a laser to exterminate the infected ones. This approach, as presented, has the drawback of using single bee images labeled and classified. This can perform well on detecting single bees on the beehive door openings or white background but fail significantly on detecting bees inside the frames (where the mites reside) due to the vast concentrations of bees on each frame. The solution to this problem proposed by this paper is to use frame images where manual bee annotation and image localization are performed before CNN training. Since regions in the image do not contain any information, image segmentation and manual identification of the ROIs can offer significantly better results.