# _Apis mellifera_ Bee Verification with IoT and Graph Neural Network
## 2 Related Works
### Works Related to Automated Visual Analysis Methods
In [21], a computer-vision system is described for monitoring the level of infestation of the Varroa destructor mite in a hive by recording a video sequence of live _Apis mellifera_ bees;this work presents lower recognition percentages compared to other research, and describes in more detail the levels of image processing; the video sequences of live _Apis mellifera_ bees are in real environments, allowing the utility of the system to be adjusted to applications in real apiaries. The system can be described as follows: a video monitoring unit with multispectral lighting and a camera placed in front of the hive allows data to be acquired for a computer-vision algorithm based on deep-learning analysis of the video stream to count the number of bees and find the position of the identified Varroa mites; the computer-vision algorithm works at various processing levels: image acquisition, low-, intermediate-, and high-level processing. Each level executes other phases during processing to obtain the result. Image acquisition is designed for real-time image acquisition and compression; low-level processing is for raw image preprocessing and temporal image matching; at the intermediate level, the following phases are executed: low-level processing, background subtraction and segmentation, bee detection, Varroa identification, and localization using a Convolutional Neural Network; finally, in high-level processing, bee tracking and counting and infestation level estimation are executed. Based on a video sequence with 1775 bees and 98 visual mites, the authors reported that the algorithm measured an infestation level of 5.80% versus a real value of 5.52%. In [22], a deep-learning approach is proposed to locate and count Varroa mites using images of the sticky boards taken by smartphone cameras. The method proposed in this research work combines hyperparameters and some image-enhancement techniques; the system achieves an average mean precision (mAP) metric of 0.9073 on the validation set. Although the metric (mAP) is high enough, the dataset proposed by the authors is composed of a limited number of images; a dataset update process, i.e., gradually increasing the dataset so that the system recognizes more images that could fit in the dataset, is not contemplated. The proposed system is described as follows: a dataset with 64 images of dimensions 8064 \(\times\) 6048 pixels (48 Mpc) taken under different lighting conditions is built; the proposed approach is composed of several successive steps; first, the input images are preprocessed to improve their quality, specifically by motion blurring using conditional adversarial networks. Then, each input image is split (for training and inference) into smaller sections (or tiles). After that, a deep-learning model is trained based on the Faster R-CNN architecture to perform Varroa mite detection, and finally, automatic refinement is performed of the predicted bounding boxes after inference. Other studies have also described the problem of parasites in bees; in [17], examples of surveillance systems that help to increase knowledge of the health of honeybees are described; video surveillance of honeybees is considered to be a tool to understand and improve bee health, identify risk factors associated with colony morbidity and mortality, disease prevention, and mitigation and early detection of threats, and discovery and characterization of new diseases and potential pathogens, including virulence and distribution. The results of this study propose the need for surveillance systems with designs that guarantee the collection of representative data, coordinate efforts, and standardize approaches.