# _Apis mellifera_ Bee Verification with IoT and Graph Neural Network
## 11 Discussion  
In this section, discussions of the presented research project have focused on the feasibility of the project and our contributions to the state of the art of the problem of Varroa detection in honeybees, the criteria used to evaluate the proposed methodology, and what have been the practical limitations that we have had for the development of this research work applied to bekeeping. As a discussion, the following question is proposed: How do our experimental results demonstrate the feasibility of using digital image processing for bee recognition in real apiary environments? The results obtained with the applied techniques and the method proposed in this research work demonstrate the feasibility of developing an automatic recognition system in real honeybee environments; based on the experiments proposed and discussed in this work, we consider that the results obtained with both techniques (even with percentages lower than 80%) are acceptable for real environments. Making a comparison between the results presented in other works in the literature, the results presented and described in this work are acceptable and demonstrate the feasibility of developing the automatic recognition system and applying it in real environments; the criteria we have considered to ensure that our research is feasible are listed below. * The development of a real-time system. We do not use any images in controlled environments, as many other works do, to offer very high recognition results; we extract the visual information from the apiary with different climatic situations, excluding only rainy days to protect our image and video capture equipment. * We present a noninvasive system in the hive. The main focus of our system was to develop noninvasive technology in the hive, to avoid stress to the bees, and not sacrifice any insect in the colony. * We developed an economic and fast system with efficient devices. The beekeeping community is not willing to spend large amounts of money on new technology, which led us to develop a system with low cost and accessible but fast devices. * The software is our own creation; the software is developed with freely available technologies, such as the C language, OpenCV, which runs on free operating systems and allows the student community of the university to offer examples of the use of this software, such as Linux in its different flavors. * The creation of four datasets that grow indefinitely as they acquire the images from the test apiaries. With these datasets, we have managed to train the proposed neural network with a large number of possible occurrences of the Varroa mite, which allows us to announce to the beekeeper community the possible cases of infection with the Varroa parasite. * The community of beekeepers in the region where the project is being carried out has requested the demonstration of the functionality of the project due to the results that have been published in different international scientific events and journals, such as [13; 34]. In previous reviews of this work, we have been asked what the contributions are to the state of the art of the problem of Varroa mite detection in honeybees; we consider that this research contributes to two areas of applied research:  
1. The development of an automatic recognition system that includes advanced techniques of artificial intelligence and the Internet of Things, and
2. experiments in uncontrolled natural environments of honeybees that allow the discovery and documentation of the factors that affect image acquisition in automatic recognition. Both areas allow us to develop technology in an area that is not documented in the literature in Mexico. Based on the above, we consider our work to be innovative and a solution to the care and preservation of honeybees in our country.