Research graph
References from Integrating image analysis and ensemble learning for predictive modeling of wood density and MC using line scanner and camera systems. Local targets link to admitted publications; unresolved targets remain external evidence.
Collection of wood quality data by X-ray densitometry: a case study with three southern pines
10.1007/s00226-015-0732-x · 2015 · External reference
10.3390/f12070949
10.3390/f12070949 · External reference
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Environmental impacts of structural lumber production in Japan
10.1186/s10086-023-02118-w · 2024 · External reference
10.3390/f10090728
10.3390/f10090728 · External reference
Juvenile Wood Characterization of Eucalyptus botryoides and E. maculata by using SilviScan
10.15376/biores.13.2.2342-2355 · 2018 · External reference
Application of near-infrared spectroscopy to forest and wood products
10.1007/s40725-023-00203-3 · 2023 · External reference
The Wood image Analysis and Dataset (WIAD): open-access visual analysis tools to advance the ecological data revolution
10.1111/2041-210x.13717 · 2021 · External reference
Image analysis to measure strain in wood and paper
10.1007/bf00225465 · 1991 · External reference
Wood identification based on macroscopic images using deep and transfer learning approaches
10.7717/peerj.17021 · 2024 · External reference
10.1186/s13007-021-00746-1
10.1186/s13007-021-00746-1 · External reference
10.1007/s11704-019-8208-z
10.1007/s11704-019-8208-z · External reference
Wood recognition using image texture features
2013 · External reference
Wood species identification based on an ensemble of deep convolution neural networks
2021 · External reference
Wood species image classification using two-dimensional convolutional neural network
10.5552/drvind.2023.0093 · 2023 · External reference
Global patterns of tree wood density
10.1111/gcb.17224 · 2024 · External reference
Probing the complexity of wood with computer vision: from pixels to properties
10.1098/rsif.2023.0492 · 2024 · External reference
MoistNet: machine vision-based deep learning models for wood chip moisture content measurement
10.1016/j.eswa.2024.125363 · 2025 · External reference
Rapid and non-destructive detection of wood density based on NIR hyperspectral imaging technology and moisture correction methods
10.1016/j.saa.2025.126410 · 2025 · External reference
Machine learning-based prediction of internal moisture variation in kiln-dried timber
10.1080/17480272.2023.2263422 · 2024 · External reference
Using machine learning to predict the density profiles of surface-densified wood based on cross-sectional images
10.1007/s00107-022-01826-2 · 2022 · External reference
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Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression
10.1007/s00107-020-01621-x · 2021 · External reference
Machine learning prediction of lignin content in poplar with Raman spectroscopy
10.1016/j.biortech.2022.126812 · 2022 · External reference
Wood Knots influence the modulus of elasticity and resistance to compression
10.1590/2179-8087.090617 · 2018 · External reference
Accurate and automated detection of surface knots on sawn timbers using YOLO-V5 model
10.15376/biores.16.3.5390-5406 · 2021 · External reference
The role of drying schedule and conditioning in moisture uniformity in wood: a machine learning approach
10.3390/polym15040792 · 2023 · External reference
Fiber quality prediction using Nir spectral data: tree-based ensemble learning vs deep neural networks
10.22382/wfs-2023-10 · 2023 · External reference
