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The paper introduces Sorometry, an AI pipeline for automated phytolith analysis, addressing the limitations of manual microscopy. It uses a multimodal fusion model combining ConvNeXt (2D images) and PointNet++ (3D point clouds) for phytolith classification and segmentation. The system achieves 77.9% accuracy in classifying 24 morphotypes and 84.5% segmentation quality, demonstrating the importance of 3D data for complex morphotype identification and enabling plant source prediction via Bayesian mixture modeling.
Ditch the microscope: AI-powered Sorometry automates phytolith analysis with impressive accuracy, turning a traditionally laborious task into a high-throughput "omics"-scale discipline.
Phytolith analysis is a crucial tool for reconstructing past vegetation and human activities, but traditional methods are severely limited by labour-intensive, time-consuming manual microscopy. To address this bottleneck, we present Sorometry: a comprehensive end-to-end artificial intelligence pipeline for the high-throughput digitisation, inference, and interpretation of phytoliths. Our workflow processes z-stacked optical microscope scans to automatically generate synchronised 2D orthoimages and 3D point clouds of individual microscopic particles. We developed a multimodal fusion model that combines ConvNeXt for 2D image analysis and PointNet++ for 3D point cloud analysis, supported by a graphical user interface for expert annotation and review. Tested on reference collections and archaeological samples from the Bolivian Amazon, our fusion model achieved a global classification accuracy of 77.9\% across 24 diagnostic morphotypes and 84.5% for segmentation quality. Crucially, the integration of 3D data proved essential for distinguishing complex morphotypes (such as grass silica short cell phytoliths) whose diagnostic features are often obscured by their orientation in 2D projections. Beyond individual object classification, Sorometry incorporates Bayesian finite mixture modelling to predict overall plant source contributions at the assemblage level, successfully identifying specific plants like maize and palms in complex mixed samples. This integrated platform transforms phytolith research into an"omics"-scale discipline, dramatically expanding analytical capacity, standardising expert judgements, and enabling reproducible, population-level characterisations of archaeological and paleoecological assemblages.
