Digitoma AI Mosquito Trap Identifies Species Amid Florida Dengue

A new trap called Digitoma uses artificial intelligence to do more than catch mosquitoes: it photographs each insect and identifies its species. According to the developers, the system reaches an accuracy of more than 98% in laboratory tests.

The reason for the project is dengue, which is spreading in Florida. Knowing which mosquito species are present in an area is a basic step in tracking such diseases, because not every species carries the same risks. Automating that identification is the idea behind the device.

How the approach works

The trap combines two steps that were traditionally separate. First, it physically captures the insects. Second, it takes pictures of them, and an AI model assesses the species from the images. The result is a stream of classified data rather than a pile of insects that someone has to sort by hand.

The 98% figure comes from laboratory conditions and is a declared value, so it should be read with caution. Field performance, where lighting, damaged specimens and mixed catches are less predictable, may differ, and the available information does not include independent verification.

Why it matters

At first glance, using AI against mosquitoes sounds like a gimmick. In practice, image recognition is well suited to the task: the job is repetitive, requires consistent classification and benefits from speed. If the accuracy holds up outside the lab, such traps could give health and mosquito-control teams faster, more detailed information about what is flying around a given area.

The spread of dengue in Florida shows why the topic is urgent. Tools that make monitoring cheaper and quicker could help authorities react sooner. Whether Digitoma will be deployed widely, and how it performs in real-world trials, remains to be seen.