Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates
Peat is formed by the accumulation of organic material in water-saturated soils. Drainage of peatlands and peat extraction contribute to carbon emissions and biodiversity loss. Most peat extracted for commercial purposes is used for energy production or as a growing substrate. Many countries aim to...
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460225000159/type/journal_article |
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| author | Serge Zaugg Camille Vögeli Lena Märki Clément Duckert Edward A.D. Mitchell |
| author_facet | Serge Zaugg Camille Vögeli Lena Märki Clément Duckert Edward A.D. Mitchell |
| author_sort | Serge Zaugg |
| collection | DOAJ |
| description | Peat is formed by the accumulation of organic material in water-saturated soils. Drainage of peatlands and peat extraction contribute to carbon emissions and biodiversity loss. Most peat extracted for commercial purposes is used for energy production or as a growing substrate. Many countries aim to reduce peat usage but this requires tools to detect its presence in substrates. We propose a decision support system based on deep learning to detect peat-specific testate amoeba in microscopy images. We identified six taxa that are peat-specific and frequent in European peatlands. The shells of two taxa (Archerella sp. and Amphitrema sp.) were well preserved in commercial substrate and can serve as indicators of peat presence. Images from surface and commercial samples were combined into a training set. A separate test set exclusively from commercial substrates was also defined. Both datasets were annotated and YOLOv8 models were trained to detect the shells. An ensemble of eight models was included in the decision support system. Test set performance (average precision) reached values above 0.8 for Archerella sp. and above 0.7 for Amphitrema sp. The system processes thousands of images within minutes and returns a concise list of crops of the most relevant shells. This allows a human operator to quickly make a final decision regarding peat presence. Our method enables the monitoring of peat presence in commercial substrates. It could be extended by including more species for applications in restoration ecology and paleoecology. |
| format | Article |
| id | doaj-art-36cb519aa39b45e293b527d033df56dc |
| institution | DOAJ |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-36cb519aa39b45e293b527d033df56dc2025-08-20T03:14:46ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2025.15Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substratesSerge Zaugg0https://orcid.org/0000-0002-6047-3112Camille Vögeli1Lena Märki2Clément Duckert3Edward A.D. Mitchell4Swiss Federal Institute of Metrology METAS, Bern, SwitzerlandLaboratory of Soil Biodiversity, University of Neuchâtel, Neuchâtel, SwitzerlandSwiss Federal Institute of Metrology METAS, Bern, SwitzerlandLaboratory of Soil Biodiversity, University of Neuchâtel, Neuchâtel, SwitzerlandLaboratory of Soil Biodiversity, University of Neuchâtel, Neuchâtel, SwitzerlandPeat is formed by the accumulation of organic material in water-saturated soils. Drainage of peatlands and peat extraction contribute to carbon emissions and biodiversity loss. Most peat extracted for commercial purposes is used for energy production or as a growing substrate. Many countries aim to reduce peat usage but this requires tools to detect its presence in substrates. We propose a decision support system based on deep learning to detect peat-specific testate amoeba in microscopy images. We identified six taxa that are peat-specific and frequent in European peatlands. The shells of two taxa (Archerella sp. and Amphitrema sp.) were well preserved in commercial substrate and can serve as indicators of peat presence. Images from surface and commercial samples were combined into a training set. A separate test set exclusively from commercial substrates was also defined. Both datasets were annotated and YOLOv8 models were trained to detect the shells. An ensemble of eight models was included in the decision support system. Test set performance (average precision) reached values above 0.8 for Archerella sp. and above 0.7 for Amphitrema sp. The system processes thousands of images within minutes and returns a concise list of crops of the most relevant shells. This allows a human operator to quickly make a final decision regarding peat presence. Our method enables the monitoring of peat presence in commercial substrates. It could be extended by including more species for applications in restoration ecology and paleoecology.https://www.cambridge.org/core/product/identifier/S2634460225000159/type/journal_articlecarbon cycledeep learningdecision supportmicroscopypeatlands |
| spellingShingle | Serge Zaugg Camille Vögeli Lena Märki Clément Duckert Edward A.D. Mitchell Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates Environmental Data Science carbon cycle deep learning decision support microscopy peatlands |
| title | Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates |
| title_full | Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates |
| title_fullStr | Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates |
| title_full_unstemmed | Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates |
| title_short | Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates |
| title_sort | decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates |
| topic | carbon cycle deep learning decision support microscopy peatlands |
| url | https://www.cambridge.org/core/product/identifier/S2634460225000159/type/journal_article |
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