Automated insect detection and biomass monitoring via AI and electrical field sensor technology
Abstract Insects, vital for ecosystem stability, are declining globally necessitating improved monitoring methods. Trap-based approaches are labor-intensive, invasive, and limited in scope. This study therefore presents a novel, automated, non-invasive insect monitoring system that detects atmospher...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15613-5 |
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| author | Freja Balmer Odgaard Páll Vang Kjærbo Amir Hossein Poorjam Khaled Hechmi Rubens Monteiro Luciano Niels Krebs |
| author_facet | Freja Balmer Odgaard Páll Vang Kjærbo Amir Hossein Poorjam Khaled Hechmi Rubens Monteiro Luciano Niels Krebs |
| author_sort | Freja Balmer Odgaard |
| collection | DOAJ |
| description | Abstract Insects, vital for ecosystem stability, are declining globally necessitating improved monitoring methods. Trap-based approaches are labor-intensive, invasive, and limited in scope. This study therefore presents a novel, automated, non-invasive insect monitoring system that detects atmospheric electrical field modulations caused by flying insects. In-field sensors monitor insect activity and biomass without physical trapping, using differential electric field measurements and convolutional neural networks for detection and wing-beat frequency analysis. Furthermore, a biomass algorithm that estimates taxon-specific weights is introduced. To validate this method, paired sensor and Townes Malaise trap deployments were conducted at two sites in a Danish nature reserve. Results showed moderate to strong correlations between sensors and traps, particularly at one site (Spearman’s $$\rho =0.725$$ for counts; 0.644 for biomass), supporting the method’s viability. A discrepancy in biomass estimates between methods, greater than that of counts, suggests the need for further refinement of the sensor’s biomass estimation. For inter-method consistency, sensor-sensor correlations ( $$\rho =0.758$$ for counts; 0.867 for biomass) exceeded Malaise-Malaise correlations ( $$\rho =0.597$$ for counts; 0.641 for biomass), though not significantly so ( $$P=0.304$$ for counts; $$P=0.057$$ for biomass). Overall, the study concludes that while further work is needed, this innovative approach shows promise for future insect monitoring and ecological research. |
| format | Article |
| id | doaj-art-e75bfcb18c9c4c60bcf68e22e4a131df |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e75bfcb18c9c4c60bcf68e22e4a131df2025-08-20T03:04:25ZengNature PortfolioScientific Reports2045-23222025-08-0115111410.1038/s41598-025-15613-5Automated insect detection and biomass monitoring via AI and electrical field sensor technologyFreja Balmer Odgaard0Páll Vang Kjærbo1Amir Hossein Poorjam2Khaled Hechmi3Rubens Monteiro Luciano4Niels Krebs5FaunaPhotonicsFaunaPhotonicsFaunaPhotonicsFaunaPhotonicsFaunaPhotonicsFaunaPhotonicsAbstract Insects, vital for ecosystem stability, are declining globally necessitating improved monitoring methods. Trap-based approaches are labor-intensive, invasive, and limited in scope. This study therefore presents a novel, automated, non-invasive insect monitoring system that detects atmospheric electrical field modulations caused by flying insects. In-field sensors monitor insect activity and biomass without physical trapping, using differential electric field measurements and convolutional neural networks for detection and wing-beat frequency analysis. Furthermore, a biomass algorithm that estimates taxon-specific weights is introduced. To validate this method, paired sensor and Townes Malaise trap deployments were conducted at two sites in a Danish nature reserve. Results showed moderate to strong correlations between sensors and traps, particularly at one site (Spearman’s $$\rho =0.725$$ for counts; 0.644 for biomass), supporting the method’s viability. A discrepancy in biomass estimates between methods, greater than that of counts, suggests the need for further refinement of the sensor’s biomass estimation. For inter-method consistency, sensor-sensor correlations ( $$\rho =0.758$$ for counts; 0.867 for biomass) exceeded Malaise-Malaise correlations ( $$\rho =0.597$$ for counts; 0.641 for biomass), though not significantly so ( $$P=0.304$$ for counts; $$P=0.057$$ for biomass). Overall, the study concludes that while further work is needed, this innovative approach shows promise for future insect monitoring and ecological research.https://doi.org/10.1038/s41598-025-15613-5InsectsBiomassInsect monitoringSignal processingArtificial intelligenceConvolutional neural network |
| spellingShingle | Freja Balmer Odgaard Páll Vang Kjærbo Amir Hossein Poorjam Khaled Hechmi Rubens Monteiro Luciano Niels Krebs Automated insect detection and biomass monitoring via AI and electrical field sensor technology Scientific Reports Insects Biomass Insect monitoring Signal processing Artificial intelligence Convolutional neural network |
| title | Automated insect detection and biomass monitoring via AI and electrical field sensor technology |
| title_full | Automated insect detection and biomass monitoring via AI and electrical field sensor technology |
| title_fullStr | Automated insect detection and biomass monitoring via AI and electrical field sensor technology |
| title_full_unstemmed | Automated insect detection and biomass monitoring via AI and electrical field sensor technology |
| title_short | Automated insect detection and biomass monitoring via AI and electrical field sensor technology |
| title_sort | automated insect detection and biomass monitoring via ai and electrical field sensor technology |
| topic | Insects Biomass Insect monitoring Signal processing Artificial intelligence Convolutional neural network |
| url | https://doi.org/10.1038/s41598-025-15613-5 |
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