Automated analysis of calcareous nannofossils has progressively transformed coccolith-based paleoceanography by enabling the quantitative investigation of extremely abundant microfossil assemblages. Over the past three decades, the SYRACO (SYstème de Reconnaissance Automatique de COccolithes) framework evolved from one of the earliest neural-network–based recognition systems for micropaleontology into an integrated platform combining automated acquisition, object detection, morphometry and coccolith-mass estimation. Here, we retrace this methodological evolution from the first artificial neural networks developed in the 1990s to the current YOLOv8-based deep-learning framework.</p> <p>We describe the successive developments in imaging, segmentation and object detection that improved over time coccolith recovery, quantitative robustness and operational scalability. Particular attention is given to the transition from segmentation-dependent workflows to integrated object detection, which fundamentally changed the balance between false positives and false negatives in quantitative coccolith analysis. To address the specific constraints of paleoceanographic applications, we use class-specific confidence thresholds to control inference reliability.</p> <p>Beyond taxonomic recognition, the integration of bidirectional circular polarization imaging and automated morphometry progressively transformed SYRACO into a quantitative optical framework capable of estimating coccolith size, thickness and calcite mass on large datasets. As an illustration of the scientific applications enabled by these developments, we present a reanalysis of the EUMELI sediment-trap series from the Mauritanian upwelling system. This reanalysis reveals two short-lived coccolithophore export events during which <em>Emiliania huxleyi</em> and <em>Gephyrocapsa oceanica</em> dominated carbonate production and export, producing several grams of CaCO₃ m⁻² at 2500 m depth within about a month.</p> <p>Together, these developments illustrate how successive generations of artificial intelligence and automated microscopy transformed coccolith analysis from a labor-intensive counting procedure into a scalable quantitative approach suitable for ecological, biogeochemical and paleoceanographic investigations.