Automation of tree-ring detection and measurements using deep learning. Poláček, M., Arizpe, A., Hüther, P., Weidlich, L., Steindl, S., & Swarts, K. Methods in Ecology and Evolution, 14(9):2233–2242, 2023. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14183
Automation of tree-ring detection and measurements using deep learning [link]Paper  doi  abstract   bibtex   
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.
@article{polacek_automation_2023,
	title = {Automation of tree-ring detection and measurements using deep learning},
	volume = {14},
	copyright = {© 2023 GMI - Gregor Mendel Institute of Molecular Plant Biology. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
	issn = {2041-210X},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.14183},
	doi = {10.1111/2041-210X.14183},
	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.},
	language = {en},
	number = {9},
	urldate = {2024-03-22},
	journal = {Methods in Ecology and Evolution},
	author = {Poláček, Miroslav and Arizpe, Alexis and Hüther, Patrick and Weidlich, Lisa and Steindl, Sonja and Swarts, Kelly},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14183},
	keywords = {automation, computer vision, mask R-CNN, phenotyping, tree rings},
	pages = {2233--2242},
}
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