DiversityScanner: Robotic discovery of small invertebrates with machine learning methods

Abstract

Invertebrate biodiversity remains poorly explored although it comprises much of the terrestrial animal biomass, more than 90% of the species-level diversity and supplies many ecosystem services. The main obstacle is specimen- and species-rich samples. Traditional sorting techniques require manual handling and are slow while molecular techniques based on metabarcoding struggle with obtaining reliable abundance information. Here we present a fully automated sorting robot, which detects each specimen, images and measures it before moving it from a mixed invertebrate sample to the well of a 96-well microplate in preparation for DNA barcoding. The images are then used by a newly trained convolutional neural network (CNN) to assign the specimens to 14 particularly common, usually family-level "classes" of insects in Malaise trap samples and an "other-class" (N=15). The average assignment precision for the classes is 91.4% (75-100%). In order to obtain biomass information, the specimen images are also used to measure specimen length and estimate body volume. We outline how the DiversityScanner robot can be a key component for tackling and monitoring invertebrate diversity. The robot generates large numbers of images that become training sets for CNNs once the images are labelled with identifications based on DNA barcodes. In addition, the robot allows for taxon-specific subsampling of large invertebrate samples by only removing the specimens that belong to one of the 14 classes. We conclude that a combination of automation, machine learning, and DNA barcoding has the potential to tackle invertebrate diversity at an unprecedented scale.