Robotics, artificial intelligence and the integration of image analysis and barcode sequencing
Exciting advances are being made in the areas of robotics and image analysis for arthropods. Species identification via image analysis is currently based on convolutional neural networks (CNNs), a specific tool in the field of deep learning (DL), where complex image patterns are classified taking advantage of training sets (Valan et al., 2019, Valan, Vondráček, & Ronquist, 2021). However, the training of CNNs requires large sets of training images whereby each image has to be labelled with reliable taxonomic information. Perhaps not surprisingly, such datasets are available for bees and butterflies (Buschbacher, Ahrens, Espeland, & Steinhage, 2020), but are largely missing for the bulk of arthropods collected by standardised trapping. The challenge is to generate these sets, and this is where a combination of robotic specimen handling and HTS barcoding can help. Robotics can generate the images and HTS barcoding can sort images to putative species. Recently, Wührl et al. (2021) presented a first-generation robot for this purpose. It detects, images and measures specimens, before moving them into a 96-well microplate for DNA barcode sequencing. This approach opens the door for a transition away from multiplex barcode sequencing of all individuals toward taxonomic assignment by image recognition, as images with barcode sequences contribute to training images for machine learning. However, it remains unclear whether automated image-based approaches alone will eventually reach the approximately species-level resolution obtained with sequence data. Image-based specimen identification could, nevertheless, be used as an external validation of molecular-based diversity estimations at, for example, genus level. Similarly, image analyses can yield information on sample biomass and abundance (Ärje et al., 2020; Schneider et al., 2022, Wührl, 2021).