Automated tree ring detection of common Indiana hardwood species through deep learning: Introducing a new dataset of annotated images

Douglass, 1919 Ferguson, 1968, Bristlecone pine: Science and esthetics: A 7100-year tree-ring chronology aids scientists; Old trees draw visitors to California mountains, Science, 159, 839, 10.1126/science.159.3817.839 LaMarche, 1978, Tree-ring evidence of past climatic variability, Nature, 276, 334, 10.1038/276334a0 Ballesteros, 2011, Calibration of floodplain roughness and estimation of flood discharge based on tree-ring evidence and hydraulic modelling, J Hydrol, 403, 103, 10.1016/j.jhydrol.2011.03.045 Wiles, 1996, Tree-ring analysis and quaternary geology: Principles and recent applications, Geomorphology, 16, 259, 10.1016/S0169-555X(96)80005-5 Norell, 2011, Automatic counting of annual rings on pinus sylvestris end faces in Sawmill industry, Comput Electron Agric, 75, 231, 10.1016/j.compag.2010.11.005 Hoadleym, 1990 Shi, 2019, MtreeRing: An R package with graphical user interface for automatic measurement of tree ring widths using image processing techniques, Dendrochronologia, 58, 10.1016/j.dendro.2019.125644 Soille, 2001, Tree ring area measurements using morphological image analysis, Can J Forest Res, 31, 1074, 10.1139/x01-025 Cerda, 2007, Robust tree-ring detection, 575 Fabijańska, 2017, Towards automatic tree rings detection in images of scanned wood samples, Comput Electron Agric, 140, 279, 10.1016/j.compag.2017.06.006 Fabijańska, 2018, DeepDendro – a tree rings detector based on a deep convolutional neural network, Comput Electron Agric, 150, 353, 10.1016/j.compag.2018.05.005 Ronneberger, 2015, 234 Habite, 2021, Automatic detection of annual rings and pith location along norway spruce timber boards using conditional adversarial networks, Wood Sci Technol, 55, 461, 10.1007/s00226-021-01266-w Isola, 2017, Image-to-image translation with conditional adversarial networks Goodfellow, 2014, Generative adversarial nets, 2672 Fabijańska, 2017, A comparative study of image enhancement methods in tree-ring analysis, 69 Lin, 2017, Feature pyramid networks for object detection He, 2016, Deep residual learning for image recognition Sandler, 2018, Mobilenetv2: Inverted residuals and linear bottlenecks, 4510 Tan, 2019, EfficientNet: Rethinking model scaling for convolutional neural networks, 6105 Rahman, 2016, 234 Duque-Arias D, Velasco-Forero S, Deschaud J-E, Goulette F, Serna A, Decencière E, et al. On power Jaccard losses for semantic segmentation. In: VISAPP 2021: 16th international conference on computer vision theory and applications. 2021. Kingma, 2014 Singh, 2012, Local contrast and mean thresholding in image binarization, Int J Comput Appl, 51 Zhang, 1984, A fast parallel algorithm for thinning digital patterns, Commun ACM, 27, 236, 10.1145/357994.358023 Gazo, 2020, A fast pith detection for computed tomography scanned hardwood logs, Comput Electron Agric, 170, 10.1016/j.compag.2019.105107