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Leveraging Phytolith Research using Artificial Intelligence

Andrés G. Mejía Ramón, Kate Dudgeon, Nina Witteveen, Dolores Piperno, Michael Kloster, Luigi Palopoli, Mónica Moraes R., José M. Capriles, Umberto Lombardo

Abstract

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.

Leveraging Phytolith Research using Artificial Intelligence

Abstract

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.
Paper Structure (15 sections, 20 figures, 6 tables)

This paper contains 15 sections, 20 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Results
  3. Overview of the Sorometry workflow
  4. Digitisation, Segmentation, and Labelling
  5. Automated Classification
  6. Assemblage Analysis
  7. Discussion
  8. Conclusion
  9. Slide Scanning
  10. Pointcloud and Orthoimage Generation
  11. Object Segmentation
  12. GUI-based Expert Review
  13. Training and Dataset Generation
  14. Assemblage Statistics
  15. Assemblage Modelling

Figures (20)

  • Figure 1: Sorometry workflow pipeline, with associated data productsin blue.
  • Figure 2: Sorometry Graphical User Interface.
  • Figure 3: Two representations of the same Cyperaceae Polygonal Achene Body phytolith: (Left) 2-Dimensional RGB Orthoimage crop; (Right) 3-Dimensional Pointcloud
  • Figure 4: Digital images of micro-fossils included in the study. (a) Acute bulbosus, ACUBUL; (b) Blocky, BLO; (c) Bulliform flabellate, BULFLA; (d) Elongate entire, ELOENT; (e) Elongate sinuate, ELOSIN; (f) Elongate dendritic/dentate, ELODET/DEN; (g) Bilobate, BIL; (h) Cross, CRO; (i) Rondel, RON; (j) Saddle, SAD; (k) Decorated tabular, TABDEC; (l) Polygonal achene body, POLACH; (m) Scalloped sphere, SSP; (n) Trough, TRO; (o) Spheroid echinate, SPHECH; (p) Spheroid symmetrical, SPHSYM; (q) Ellipsoidal echinate, ELLECH; (r) Druse, DRU; (s) Non-phytolith Sponge spicule, SPO; (t) Non-phytolith Silica Microsphere, SilicaBall. Scale bar: 10 µm. The red overlay represents the automatically segmented portion of the phytolith based on the point-cloud extraction. Detailed descriptions of morphological criteria, sub-categories of diverse groups and rationale for inclusion in the study are provided in Supplementary Materials B.
  • Figure 5: Morphotype classification performance for the Fusion model, showing percent true values classified as a given predicted value.
  • ...and 15 more figures