Automation of tree‐ring detection and measurements using deep learning
Abstract Core samples from trees are a critical reservoir of ecological information, informing our understanding of past climates, as well as contemporary ecosystem responses to global change. Manual measurements of annual growth rings in trees are slow, labour‐intensive and subject to human bias, h...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.14183 |
| _version_ | 1827824176120987648 |
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| author | Miroslav Poláček Alexis Arizpe Patrick Hüther Lisa Weidlich Sonja Steindl Kelly Swarts |
| author_facet | Miroslav Poláček Alexis Arizpe Patrick Hüther Lisa Weidlich Sonja Steindl Kelly Swarts |
| author_sort | Miroslav Poláček |
| collection | DOAJ |
| description | Abstract Core samples from trees are a critical reservoir of ecological information, informing our understanding of past climates, as well as contemporary ecosystem responses to global change. Manual measurements of annual growth rings in trees are slow, labour‐intensive and subject to human bias, hindering the generation of big datasets. We present an alternative, neural network‐based implementation that automates detection and measurement of tree‐ring boundaries from coniferous species. We trained our Mask R‐CNN extensively on over 8000 manually annotated ring boundaries from microscope‐imaged Norway Spruce Picea abies increment cores. We assessed the performance of the trained model after post‐processing on real‐world data generated from our core processing pipeline. The CNN after post‐processing performed well, with recognition of over 98% of ring boundaries (recall) with a precision in detection of 96% when tested on real‐world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were 98% correlated with human measurements of the same samples. Tests on other three conifer species demonstrate that the CNN generalizes well to other species with similar structure. We demonstrate the efficacy of automating the measurement of growth increment in tree core samples. Our CNN‐based system provides high predictive performance in terms of both tree‐ring detection and growth rate determination. Our application is readily deployable as a Docker container and requires only basic command line skills. Additionally, an easy re‐training option allows users to expand capabilities to other wood types. Application outputs include both editable annotations of predictions as well as ring‐width measurements in a commonly used .pos format, facilitating the efficient generation of large ring‐width measurement datasets from increment core samples, an important source of environmental data. |
| first_indexed | 2024-03-12T02:22:14Z |
| format | Article |
| id | doaj.art-8141a871e73e4b3fa04a8ed47d75c3c4 |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-03-12T02:22:14Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-8141a871e73e4b3fa04a8ed47d75c3c42023-09-06T04:43:40ZengWileyMethods in Ecology and Evolution2041-210X2023-09-011492233224210.1111/2041-210X.14183Automation of tree‐ring detection and measurements using deep learningMiroslav Poláček0Alexis Arizpe1Patrick Hüther2Lisa Weidlich3Sonja Steindl4Kelly Swarts5Austrian Academy of Sciences Gregor Mendel Institute Vienna AustriaAustrian Academy of Sciences Gregor Mendel Institute Vienna AustriaAustrian Academy of Sciences Gregor Mendel Institute Vienna AustriaAustrian Academy of Sciences Gregor Mendel Institute Vienna AustriaAustrian Academy of Sciences Gregor Mendel Institute Vienna AustriaAustrian Academy of Sciences Gregor Mendel Institute Vienna AustriaAbstract Core samples from trees are a critical reservoir of ecological information, informing our understanding of past climates, as well as contemporary ecosystem responses to global change. Manual measurements of annual growth rings in trees are slow, labour‐intensive and subject to human bias, hindering the generation of big datasets. We present an alternative, neural network‐based implementation that automates detection and measurement of tree‐ring boundaries from coniferous species. We trained our Mask R‐CNN extensively on over 8000 manually annotated ring boundaries from microscope‐imaged Norway Spruce Picea abies increment cores. We assessed the performance of the trained model after post‐processing on real‐world data generated from our core processing pipeline. The CNN after post‐processing performed well, with recognition of over 98% of ring boundaries (recall) with a precision in detection of 96% when tested on real‐world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were 98% correlated with human measurements of the same samples. Tests on other three conifer species demonstrate that the CNN generalizes well to other species with similar structure. We demonstrate the efficacy of automating the measurement of growth increment in tree core samples. Our CNN‐based system provides high predictive performance in terms of both tree‐ring detection and growth rate determination. Our application is readily deployable as a Docker container and requires only basic command line skills. Additionally, an easy re‐training option allows users to expand capabilities to other wood types. Application outputs include both editable annotations of predictions as well as ring‐width measurements in a commonly used .pos format, facilitating the efficient generation of large ring‐width measurement datasets from increment core samples, an important source of environmental data.https://doi.org/10.1111/2041-210X.14183automationcomputer visionmask R‐CNNphenotypingtree rings |
| spellingShingle | Miroslav Poláček Alexis Arizpe Patrick Hüther Lisa Weidlich Sonja Steindl Kelly Swarts Automation of tree‐ring detection and measurements using deep learning Methods in Ecology and Evolution automation computer vision mask R‐CNN phenotyping tree rings |
| title | Automation of tree‐ring detection and measurements using deep learning |
| title_full | Automation of tree‐ring detection and measurements using deep learning |
| title_fullStr | Automation of tree‐ring detection and measurements using deep learning |
| title_full_unstemmed | Automation of tree‐ring detection and measurements using deep learning |
| title_short | Automation of tree‐ring detection and measurements using deep learning |
| title_sort | automation of tree ring detection and measurements using deep learning |
| topic | automation computer vision mask R‐CNN phenotyping tree rings |
| url | https://doi.org/10.1111/2041-210X.14183 |
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