# _Apis mellifera_ Bee Verification with IoT and Graph Neural Network
###### Abstract  
Automatic recognition systems (ARS) have been proposed in scientific and technological research for the care and preservation of endangered species; these systems, consisting of Internet of Things (IoT) devices and object-recognition techniques with artificial intelligence (AI), have emerged as proposed solutions to detect and prevent parasite attacks on _Apis mellifera_ bees. This article presents a pilot ARS for the recognition and analysis of honeybees at the hive entrance using IoT devices and automatic object-recognition techniques, for the early detection of the Varroa mite in test apiaries. Two object-recognition techniques, namely the k-Nearest Neighbor Algorithm (kNN) and Graph Neural Network (GNN), were evaluated with an image dataset of 600 images from a single beehive. The results of the experiments show the viability of using GNN in real environments. GNN has greater accuracy in bee recognition, but with greater processing time, while the kNN classifier requires fewer processing resources but has lower recognition accuracy. Apis mellifera; automatic recognition system; graph neural network; k-nearest neighbor Article  
Apis mellifera; [PERSON] and [PERSON]  
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ing practices [5], integrated pest management (IPM) [6], international monitoring, and therapeutic alternatives [7], all seek to increase the effectiveness of Varroa mite control. Currently, the Varroa mite is detected by beekeepers through two types of visual checks [8; 9], double-sieve sampling and powdered sugar sampling. Both checks have been documented by the Food and Agriculture Organization of the United Nations (FAO) [10]. When carried out in apiaries, both of these checks imply the opening of the hive at a time that the beekeeper decides. After the hive is open, hundreds of bees are collected and killed with isopropyl alcohol, then discarded with filters that show the existence or absence of the Varroa ectoparasite [8; 9; 11]. The manual collection of the ectoparasite [12] using substances applied to a group of approximately 100 bees allows for the detection of the infestation in the hive. If the ectoparasite is detected, the application of effective acaricides (fluvalinate-tau, coumaphos, or mitraz) [7] is carried out. Another method for detecting the Varro mite, in [11], is a visual analysis of the hive followed by the detection and counting of the number of mites on a sliding unit, then the estimation of the possible level of infestation by a heuristic calculation from the count. These controls have three main disadvantages: first, they cause stress, loss of the queen, and loss of larvae in gestation in the colony due to the opening of the hive; second, every time manual control of the Varroa mite is carried out, hundreds of productive-age bees are sacrificed in each hive; and third, the effectiveness of the control is questionable, as it is based solely on the periodicity of the controls carried out by the beekeeper in the apiary. Thus, if few visual controls are carried out per year, the risk of attack by the Varroa mite increases; contrary to the above, however, if many controls are carried out, the risk of stress, escape of queens, and damage to larvae (unborn bees) increases. The automation of the Varroa mite verification procedures in hives is a feasible development of an automatic recognition systems (ARS). Automatic recognition systems (ARS) consisting of intelligent software systems that manipulate miniaturized electrical, electronic, and mechanical devices, have emerged as an alternative to manual visual inspections. Pattern-recognition methods that allow for automated visual analysis have proven effective in different areas of scientific research. In the search for solutions to the Varroa ectoparasite, several works have been proposed [8; 9; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22].