An insect-sorting robot could help scientists grapple with the global biodiversity crisis.

What’s new: An automated insect classifier sucks in tiny arthropods, classifies them, and maps their most important identifying features. It was developed by researchers at Karlsruhe Institute of Technology, Berlin Natural History Museum, Bavarian State Collection of Zoology, Sapienza University of Rome, and National University of Singapore.

How it works: The bot integrates systems that transport insects in and out, snap photos of them, and process the images. A touch screen serves as the user interface and displays model output. The authors pretrained a VGG19 convolutional neural network on ImageNet and fine-tuned it using 4,325 images of insects plus augmentations.

Users place a petri dish full of unsorted, deceased insects in the machine’s receptacle. A downward-facing camera feeds a model that determines which shapes in the container are insects and helps a suction-tipped robot arm pick one up.
A three-axis robot driven by a Raspberry Pi computer transfers the specimen to a plate, where a second camera takes a detailed photo. The VGG19 accepts the image and classifies the bug.
The researchers used CAM to create a heat map of the parts of the image that were used to classify an image.
The robot moves the specimen to a second tray for DNA sequencing. The system appends its DNA information to the file containing its picture, identification, and measurements.

Results: In testing, the system scored an average of 91.4 percent precision across all species — good but not up to the level of a human expert.

Behind the news: This is just the latest use of AI in the time-consuming task of insect identification.

Researchers from Oregon State University developed a system that transports water-borne insects via a fluid-filled tube to a camera for identification.
A team of Israeli inventors filed a patent for a system that differentiates male from female mosquitoes.
A device designed by researchers in Denmark and Finland uses neural networks to identify insects, but it requires users to feed it individual specimens by hand.
Why it matters: The World Economic Forum lists loss of biodiversity as one of the biggest threats to civilization worldwide. Insects are a key bellwether, but their tiny size and huge numbers make it difficult to track their wellbeing at a species level. Automated approaches to evaluating insect populations could help scientists assemble an accurate picture.

We’re thinking: If this system stopped working, someone would have to debug it.

Bavarian State Collection of Zoology

Bugbot

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