Edge computer vision produces microarthropod-based high-throughput biodiversity metrics

Dombos, M.; Hartke, T. R.; Toth, Z.; Bekesi, G. B.; Scherber, C.; Schmera, D.; Zawiasa, B.; Sipocz, L.; Gergocs-Winkler, V.; Florian, N.; Prenzel, V. C.; Lejoly, J.; Newbert, M.; Bussell, J.; Serrati, L.; Veres, A.; Juhasz, A.; Juhos, K.; Kaasik, R.; Vasileiadis, V.

doi:10.1101/2025.09.09.675044

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bioRxiv · 10.1101/2025.09.09.675044

Edge computer vision produces microarthropod-based high-throughput biodiversity metrics

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Abstract

Soil fauna is crucial for carbon cycling, controlling organic matter decomposition and contributing to ecosystem services1-7. Soil microarthropods are top-down regulators in the decomposer food web and serve as fundamental indicators of soil health. Yet, routine field-level monitoring remains restricted to resource-intensive research, leaving a critical gap in arable land management and policy. To address this, we developed Edapholog(R) extractor, a fully automated laboratory device that detects and identifies live soil microarthropods via real-time, AI-based image analysis, enabling taxonomic identification. Here, we demonstrate its accuracy across 319 arable fields spanning ten European countries. We show that computer vision can support ecological interpretation in arable systems and long-term studies on conservation tillage and cover cropping, offering a scalable tool for integrating soil biodiversity metrics into regenerative agriculture and carbon farming. We validated the system against classical taxonomy and found that across [~]35,000 microarthropod individuals, the device achieved an overall accuracy of 86%, sensitivity of 75%, and specificity of 99% compared to manual identifications. Community composition analyses revealed high similarities (83%), with minimal richness differences (7%) and low species replacement (13%) across countries, indicating that the AI does not introduce taxonomic bias. When applied in a long-term field experiment, the system detected significant taxon-specific responses to conservation tillage, with effect sizes ranging from 0.5 to 4. Total abundance, richness, and a soil biological health index were 39%, 47%, and 150% higher, respectively, under conservation tillage compared to conventional ploughing. These effects were statistically consistent between the automated and classical methods. However, while manual microscopy required several hours per sample, the AI-based system delivered immediate results without the need for taxonomic expertise. Edapholog(R) extractor offers exciting opportunities for rapid, scalable soil biodiversity monitoring for future sustainable land management.

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Dombos, M., Hartke, T. R., Toth, Z., Bekesi, G. B., Scherber, C., Schmera, D., Zawiasa, B., Sipocz, L., Gergocs-Winkler, V., Florian, N., Prenzel, V. C., Lejoly, J., Newbert, M., Bussell, J., Serrati, L., Veres, A., Juhasz, A., Juhos, K., Kaasik, R., Vasileiadis, V.. 2025-09-14. Edge computer vision produces microarthropod-based high-throughput biodiversity metrics. https://doi.org/10.1101/2025.09.09.675044

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