With biodiversity in decline around the world, researchers are desperate to catalog all of Earth’s insects and other invertebrates, which represent 90% of the 9 million species yet to be named. To do so, scientists typically face long hours in the lab sorting through the specimens they collected.
Enter DiversityScanner. The approach involves a robot, which plucks individual insects and other small creatures one at a time from trays and photographs them. A computer then uses a type of artificial intelligence known as machine learning to compare each one’s legs, antennae, and other features to known specimens.
The technology then imposes a color code, or heat map, over the image (see above). The warmer the color, say, red, the more the computer program depended on that body part to make a call on the type of insect it was. This heat map makes it easier for researchers checking the identification to see what the program’s “thought” process was.
The robot then moves each insect into a plate with 96 tiny wells, readying these specimens for DNA sequencing. The resulting species-identifying piece of sequence—a “DNA barcode”—is linked to the image in a database of all the cataloged specimens.
Although not quite as good as a human expert, the approach accurately classifies insects 91% of the time, the designers of the technology report in a study posted to the preprint server bioRxiv. That accuracy will improve as more specimens are added to the database, they note.
The researchers have made the software and 3D printing plans for the technology openly available. And, as the scientists describe in a second preprint, they have simplified the sequencing steps and software so that developing countries and small organizations can take advantage of it—96 insects at a time.
