# _Apis mellifera_ Bee Verification with IoT and Graph Neural Network
###### Abstract
These works are focused on the development of computer systems for the detection and reduction of the harmful effects of the Varroa ectoparasite with different analysis and object-recognition techniques in images applied in real environments. For the development of automatic recognition systems, three areas of research must be considered: first, the real environment where the system must operate; second, the use of digital image-processing techniques to extract image features; and finally, the methods of object recognition in the images. We refer to each of the areas below according to the research work carried out in visual-inspection systems in apiaries. Real environments have a certain degree of complexity when implementing computer systems for outdoor environments, as the images are influenced by different factors such as lighting, the area of the real environment, and the movement of objects [15; 23], as well as data-processing speeds greater than conventional ones [24]. Digital image-processing techniques for the identification of regions and extraction of object features from the image [25], have been successfully used in automatic recognition systems; in this way, these techniques are used in systems applied to video inspection in beehives. For example, ref. [14] measured activity at the hive entrance by processing a set of frames, ref. [15] detected pollen-carrying bees, parasites were detected in [8], and Varroa mites were detected in [9; 19; 20; 21; 22]. In bee surveillance systems, two methods have been used for recognition: Gaussian [14; 15] and neural networks [8; 15; 21; 22]. The neural-network models that have been used successfully have been the following: ref. [9] used a pre-trained Convolutional Neural Network(CNN) model to detect bees; ref. [20] used YOLO with a single neural network to predict bounding boxes; ref. [21] used a Convolutional Neural Network for bee detection and Varroa identification, and ref. [22] used a deep-learning model based on Faster CNN (R-CNN) architecture to perform Varroa mite detection. A different neural-network model from the previous ones is the Graph Neural Network (GNN) initially proposed in [26]; this type of network represents, in a natural way and through graph structures, various application areas [27]. GNNs for object recognition in images are being widely used. For example, in [28,29] they are used for image classification; in [30], edge features extracted from objects contained in images are used in object recognition with GNNs; in [31], GNNs are used for extracting planar roof structures from high-resolution images; in [32], GNNs are used for symbol detection in document images. GNN applications are not only limited to applications in images; they have also been used for graph learning representation [33]. Although various neural-network models have been applied to the detection of Varroa mites in bees, the problem remains open. Then, considering the examples from the analyzed literature and the problem of the Varroa mite existing in the environment, in addition to the traditional practices of Varroa mite control (both described in the previous paragraphs), we propose in this research work the development of a noninvasive visual-inspection system, retaining the standards of care of bees, which can remain in an apiary for an indefinite period of time, performing the tasks of detecting the Varroa parasite. This work describes the implementation of an automatic recognition system that acquires real images of test apiaries during the four seasons of the year, with image-acquisition equipment using the Internet of Things at the entrance of the hive; the images are sent through a telecommunications system to be processed and analyzed in a server cluster. Finally, the bees are recognized in the images using DIP and GNN techniques, and a subsequent analysis of the abdomen of the _Apis mellifera_ bee is carried out to detect the ectoparasitic Varroa.