Machine learning and related types of Artificial Intelligence(AI) are used to work to help solve various problems but it still seems surprising that it could be used to help stop the alarming decline in bee populations across the globe.

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The Varroa mite Wikipedia describes the Varrao mite as follows: ” The Varroa mite can only reproduce in a honey bee colony. It attaches to the body of the bee and weakens the bee by sucking fat bodies [1]. In this process, RNA viruses such as the deformed wing virus (DWV) spread to bees. A significant mite infestation will lead to the death of a honey bee colony, usually in the late autumn through early spring. The Varroa mite is the parasite with the most pronounced economic impact on the beekeeping industry. Varroa is considered to be one of multiple stress factors[2] contributing to the higher levels of bee losses around the world.”

A recent article claims that the mite rarely kills a bee outright but weakens the bee by sucking blood and weakening it making it susceptible to diseases and causes young to be born weak and deformed. In time this can lead to colony collapse. One problem is that you may not even see the mites as they are only a millimeter or so across. An infestation may not be discovered for some time. As shown on the appended video beekeepers put a flat surface beneath the hive and pull it out to inspect it to find tiny bodies of the mites. It is painstaking and time-consuming work. How machine learning can help Machine learning models are good at sorting through data that is “noisy” such as the flat surface with the varroa mites on it but covered in all sorts of other debris. The machine can be taught to identify the shape of the mites, and count them.

Apizoom Students in Switzerland at the Ecole Polytechnique Federale at Lausanne(EPFL) have created an image recognition device named ApiZoom. When trained on images of mites through a photo, the device can recognize any visible mite bodies in seconds. All a beekeeper has to do is take a photo with a smartphone and upload it to the EPFL system. The EPFL project was begun back in 2017. The model has been trained with tens of thousands of images that have made it progressively better at its job. The success rate of detection is now about 90 percent about the same as humans achieve. The project now intends to distribute the app as widely as it can. Alain Burgnon of the EPFL project said: “We envisage two phases: a web solution, then a smartphone solution. These two solutions allow to estimate the rate of infestation of a hive, but if the application is used on a large scale, of a region. By collecting automatic and comprehensive data, it is not impossible to make new findings about a region or atypical practices of a beekeeper, and also possible mutations of the Varroa mites.”

This kind of systematic data collection would be a major help for coordinating response to infestations at a national level. No doubt ApiZoom could be used globally not just in Switzerland. Apizoom is being spun off as a separate company by Bugnon. There are many ways of dealing with an infestation as described in the Wikipedia article on the virus. Some bee types are resistant to the mite and perhaps more of those bees will be used to produce honey. However, the Apizoom app will no doubt help out to control the mite.[…]

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