Research outputs

Publications

Computer vision, scientific imaging, and AI for biological research.

Published papers

Entomologia Experimentalis et Applicata · 2026Published

An AI-driven biomass estimation tool enabling population- and specimen-level variation analyses: A case study on parasitoid flies

Applies InsectMorphoAI to parasitoid flies to compare image-derived body size and biomass proxies across two surveys conducted 24 years apart.

Ascenzi, A.^*, Shirali, H.^*, Di Lorenzo, N., Nania, D., Wührl, L., Pylatiuk, C., Meier, R., & Cerretti, P.

^* Equal contribution.

Published online 8 September 2026.

2026 IEEE International Conference on Mechatronics and Automation (ICMA) · 2026Published

Automated Mechatronic Multi-Camera System for 360° Digitization of Small Specimens

Automates multi-perspective imaging of small fluid-preserved specimens using three cameras, focus stacking, and controlled rotation.

Klug, N., Herrmann, F., Händle, F., Wührl, L., Shirali, H., Kramer, M., Saur, L., Meier, R., Pylatiuk, C.

pp. 617-622

Paper

Journal of Hazardous Materials Advances · 2026Published

Classification of microplastic particles in water using polarized light scattering and machine learning methods

Investigates machine learning for classifying microplastic particles from polarized light scattering.

Saur, L., von Pawlowski, M., Gengenbach, U., Sieber, I., Shirali, H., Wührl, L., Weng, X., Kiko, R., & Pylatiuk, C.

23, 101343.

Paper

Earlier preprint (2025)

Automatisierungstechnik · 2026Published

Automated photogrammetric close-range imaging of small invertebrates using acoustic levitation

Explores acoustic levitation for automated close-range imaging of small invertebrates.

Klug, N., Kramer, M., Mazrek, F., Wührl, L., Shirali, H., Meier, R., & Pylatiuk, C.

74(7), 564–576.

Earlier preprint (2024)

InsectMorphoAI interface showing a fly, anatomical segmentation, and volume estimation. — InsectMorphoAI software interface.

Ecological Informatics · 2026Published

InsectMorphoAI: A deep learning framework for automated insect morphometrics and biomass estimation with taxon-specific volumetric validation

Estimates insect length, volume, and biomass from 2D images, with taxon-specific volumetric validation.

Shirali, H., Ascenzi, A., Wührl, L., Beyer, N., Di Lorenzo, N., Vaccarella, E., Klug, N., Meier, R., Cerretti, P., & Pylatiuk, C.

96, 103854.

Earlier preprint (2025)

Twelve labelled Agrilus beetle specimens with different body shapes and colours; specimens are not to scale. — Fig. 1, Caruso et al. (2026), CC BY 4.0. Specimens not to scale.

NeoBiota · 2026Published

Image-based recognition using advanced neural networks can aid surveillance of Agrilus jewel beetles

Tests neural networks for image-based identification of Agrilus beetles to support forest pest surveillance.

Caruso, V.^*, Shirali, H.^* · Full author list

Caruso, V.^*, Shirali, H.^*, Bouget, C., Cerretti, P., Curletti, G., de Groot, M., Groznik, E., Gutowski, J. M., Pylatiuk, C., Plewa, R., Roques, A., Sallé, A., Sweeney, J., Van Rooyen, K., Wührl, L., & Rassati, D.

^* Equal contribution.

105, 319-336.

Four specimen images illustrating orientation classification and model attention on fly anatomy. — Shirali et al. (2026), CC BY.

Systematic Entomology · 2026Published

Automated specimen triage for dark taxa: Deep learning enables orientation, sex identification and anatomical segmentation from robotic imaging

Uses deep learning to identify specimen orientation and sex and segment anatomy in images of morphologically challenging flies.

Shirali, H., Wührl, L., Lee, L., Klug, N., Meier, R., Pylatiuk, C., & Hartop, E.

51, e70039.

Automatisierungstechnik · 2024Published

Automated handling of biological objects with a flexible gripper for biodiversity research

Introduces a flexible gripper for automated handling of biological specimens in biodiversity research.

Wührl, L., Keller, L., Klug, N., Shirali, H., Meier, R., & Pylatiuk, C.

72(7), 672-678.

Paper

Invertebrate Systematics · 2024Published

Image-based recognition of parasitoid wasps using advanced neural networks

Investigates image-based neural networks for identifying parasitoid wasps.

Shirali, H., Hübner, J. J., Both, R., Raupach, M. J., Reischl, M., Schmidt, S., & Pylatiuk, C.

38(6), IS24011.

Paper

Preprints & Papers in Submission

Publication status is indicated for each work. Earlier preprints of published articles are linked with their journal version.

Authorea · 2026Preprint · submitted

Deep learning enables automated species identification of ground beetles (Carabidae) for biodiversity monitoring

Evaluates deep-learning classifiers for ground beetle identification and examines how specimen segmentation reduces reliance on image backgrounds.

Zahiri, R.^*, Denner, A.^*, Shirali, H., Beyer, N., Pylatiuk, C., Müller, P., von Reumont, B., Husemann, M.

^* Equal contribution.

Paper

Manuscript · 2026Submitted

Neglect of small-bodied Diptera in sequencing and taxonomy hinders the identification of a substantial fraction of insect biomass

Combines DNA barcodes and image-derived body lengths to assess gaps in fly reference libraries, using cumulative body length as a biomass proxy.

Ascenzi, A.^*, Shirali, H.^*, Feng, V., Srivathsan, A., Pylatiuk, C., Meier, R., Cerretti, P.

^* Equal contribution.

Entomoscope photomicroscope with camera, motorized vertical stage, and illuminated specimen platform. — Entomoscope 2.0 device photograph, Shirali et al.

bioRxiv · 2026Preprint

Entomoscope 2.0 and ENIMAS 2.0: An open-source, AI-integrated platform for rapid and affordable insect digitization

Combines an affordable photomicroscope with open-source software for automated insect imaging and AI analysis.

Shirali, H., Boese, N. E., Kramer, M., Wang, J., Klug, N., Mikut, R., Meier, R., & Pylatiuk, C.

Manuscript · 2025Submitted

Close-range photogrammetry for 3D reconstruction of small objects: A systematic review

Reviews image acquisition, reconstruction methods, and validation practices for photogrammetry of objects smaller than 10 cm.

Klug, N., Petani, L., Wührl, L., Shirali, H., Meier, R., Pylatiuk, C.

In Preparation

Ongoing work; not yet submitted or peer reviewed.

Manuscript · 2026In preparation

Auto-BIT: Advancing beetle surveillance through automated imaging

Develops a modular imaging add-on for beetle traps that captures individual insects in free fall using laser triggering and a global-shutter camera.

Kramer, M., Dehon, F., Wührl, L., Klug, N., Shirali, H., Roques, A., Jactel, H., Rassati, D., Battisti, A., Pylatiuk, C.