Computer vision-based estimation of small arthropod biomass
4 affiliations
- 1Finnish Environment Institute (Syke), Helsinki, Finland; Corresponding author.
- 2University of Duisburg–Essen, Aquatic Ecosystem Research, Essen, Germany; University of Duisburg–Essen, Centre for Water and Environmental Research (ZWU), Essen, Germany
- 3Aarhus University, Department of Ecoscience, Aarhus, Denmark
- 4University of Jyväskylä, Faculty of Information Technology, Jyväskylä, Finland
Abstract
The ability to estimate invertebrate biomass using only images could help scaling up quantitative biodiversity monitoring efforts. Computer vision-based methods have the potential to omit the manual, time-consuming, and destructive process of dry weighing specimens. We present two approaches for dry mass estimation that do not require additional manual effort apart from imaging the specimens: fitting a linear model with novel predictors, automatically calculated by an imaging device, and training a family of end-to-end deep neural networks for the task, using single-view, multi-view, and metadata-aware architectures. We propose using area and sinking speed as predictors. These can be calculated with BIODISCOVER, which is a dual-camera system that captures image sequences of specimens sinking in an ethanol column. For this study, we collected a large dataset of dry mass measurement and image sequence pairs to train and evaluate models. We show that our methods can estimate specimen dry mass even with complex and visually diverse specimen morphologies. Combined with automatic taxonomic classification, our approach achieves a median individual error of 10%–20% and produces group-level dry mass distributions that do not differ significantly from observed distributions. We highlight the importance of choosing appropriate evaluation metrics, and encourage using both percentage errors and absolute errors as metrics, because they measure different properties. We also explore different optimization losses, data augmentation methods, and model architectures for training deep-learning models.
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BibTeX
@article{impio2026computer,
title = {{Computer vision-based estimation of small arthropod biomass}},
author = {Mikko Impiö and Philipp M. Rehsen and Jarrett Blair and Cecilie Mielec and Arne J. Beermann and Florian Leese and Toke T. Høye and Jenni Raitoharju},
journal = {Ecological Informatics},
year = {2026},
month = oct,
doi = {10.1016/j.ecoinf.2026.104091},
url = {https://doi.org/10.1016/j.ecoinf.2026.104091},
note = {CC BY},
}
RIS
TY - JOUR
TI - Computer vision-based estimation of small arthropod biomass
AU - Mikko Impiö
AU - Philipp M. Rehsen
AU - Jarrett Blair
AU - Cecilie Mielec
AU - Arne J. Beermann
AU - Florian Leese
AU - Toke T. Høye
AU - Jenni Raitoharju
PY - 2026
DA - 2026/10/30
JO - Ecological Informatics
DO - 10.1016/j.ecoinf.2026.104091
UR - https://doi.org/10.1016/j.ecoinf.2026.104091
AB - The ability to estimate invertebrate biomass using only images could help scaling up quantitative biodiversity monitoring efforts. Computer vision-based methods have the potential to omit the manual, time-consuming, and destructive process of dry weighing specimens. We present two approaches for dry mass estimation that do not require additional manual effort apart from imaging the specimens: fitting a linear model with novel predictors, automatically calculated by an imaging device, and training a family of end-to-end deep neural networks for the task, using single-view, multi-view, and metadata-aware architectures. We propose using area and sinking speed as predictors. These can be calculated with BIODISCOVER, which is a dual-camera system that captures image sequences of specimens sinking in an ethanol column. For this study, we collected a large dataset of dry mass measurement and image sequence pairs to train and evaluate models. We show that our methods can estimate specimen dry mass even with complex and visually diverse specimen morphologies. Combined with automatic taxonomic classification, our approach achieves a median individual error of 10%–20% and produces group-level dry mass distributions that do not differ significantly from observed distributions. We highlight the importance of choosing appropriate evaluation metrics, and encourage using both percentage errors and absolute errors as metrics, because they measure different properties. We also explore different optimization losses, data augmentation methods, and model architectures for training deep-learning models.
SN - 1574-9541
SN - 1878-0512
ER -