Artificial intelligence applied to the automated detection and identification of Devonian miospores

Abstract. Over the last few years, rapid advances in artificial intelligence, especially the application of convolutional neural networks (CNNs), have created new opportunities for automated microscopic image identification. Here we introduce a two-step pipeline for the automated detection and identification of Devonian miospore species by integrating object detection with downstream classification. Our workflow focused on three biostratigraphically important species occurring in the upper Frasnian sedimentary sequences of Armenia: Teichertospora torquata, Geminospora lemurata, and Samarisporites triangulatus. A YOLOv11-based detector localized initially individual miospore specimens on slide images, producing background-free crops and a size measure based on bounding-box area. The detector reached 98 % precision on the test set. The cropped spore images were then classified with the use of three different CNN architectures (VGG16, ResNet-18, and EfficientNet-B0), with size included as an additional numeric feature alongside the image input. Among the three tested models, ResNet-18 performed the best, achieving 87.9 % of classification accuracy. Our results show that combining modern detection with compact classification and morphometric information provides a practical route toward semi-automated palynology for Devonian spores.