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bioRxiv · 10.1101/2025.05.22.655251

Deep Learning-Based Methods for Automated Estimation of Insect Length, Volume, and Biomass

Shirali, H.·Ascenzi, A.·Wuehrl, L.·Beyer, N.·DI LORENZO, N.·VACCARELLA, E.·KLUG, N.·MEIER, R.·CERRETTI, P.·Pylatiuk, C.
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Abstract

We present InsectMorphoAI, an open-source, user-friendly software package that automates the measurement of insects from 2D images. The software addresses the need for high-throughput, non-invasive alternatives to laborious and often destructive manual measurement methods. InsectMorphoAI provides two analysis modes: a rapid, general-purpose method using oriented bounding boxes for linear length estimation across diverse taxa, and a high-precision, taxon-specific instance segmentation method for detailed curvilinear length, volume, and biomass estimation. We demonstrate the softwares accuracy, showing that the volume estimates from the segmentation module are strongly correlated with dry weight (R = 0.907), and the general length module achieves a mean absolute error corresponding to ~2.3% of the average specimen length. InsectMorphoAI is distributed with a graphical user interface and is freely available, with straightforward installation via a Docker container or a native Python environment. By streamlining data acquisition, InsectMorphoAI facilitates the integration of detailed trait data into large-scale ecological research, from biodiversity monitoring to functional trait analysis.

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Shirali, H., Ascenzi, A., Wuehrl, L., Beyer, N., DI LORENZO, N., VACCARELLA, E., KLUG, N., MEIER, R., CERRETTI, P., Pylatiuk, C.. 2025-05-27. Deep Learning-Based Methods for Automated Estimation of Insect Length, Volume, and Biomass. https://doi.org/10.1101/2025.05.22.655251

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