Abstract Robotic specimen processing is transforming biodiversity research by replacing manual handling with scalable systems that produce high‐quality specimen images. We demonstrate that these images can be used to efficiently extract key biological information and guide targeted specimen processing by applying deep learning methods. Using a model dark taxon, Phoridae (Diptera), we show that deep learning can perform three core tasks: sex identification, determining specimen orientation and anatomical segmentation. Sex identification allows selective retention of diagnostically informative specimens, avoiding wasted effort on non‐diagnostic individuals. Orientation classification enables photos of specimens with the desired orientation to be processed immediately, while suboptimally oriented specimens can be repositioned. Anatomical segmentation enables targeted processing of specimen photos that show diagnostic features. Comparative analysis of model architectures shows task‐specific selection is crucial: a Convolutional Neural Network (CNN) achieved an accuracy of 0.94 for orientation, a Vision Transformer achieved 0.88 for sex and a U‐Net precisely segmented nine anatomical regions with a mean IoU of 0.78. These results demonstrate that robotic imaging combined with deep learning helps in developing a high‐throughput taxonomy for dark taxa, improving efficiency and utility.
Shirali et al. (Thu,) studied this question.