Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

Ping-an, Sun

doi:10.1155/2022/4878090

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…This modeling approach is based on regression analysis [21,24,25]. The development of machine learning (ML)-based regression models has been increasingly explored [30][31][32][33]. ML models can handle complex datasets that encompass nonlinear or missing data.…”

Section: Introductionmentioning

confidence: 99%

Electrical Properties at Multi-Frequencies for Analysis of Physical and Anatomical Properties of Fast-Growing Standing Teak Trees at Various Ages

Agustiningrum,

Siregar,

Damayanti

et al. 2024

Forests

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Fast-growing teak trees are cultivated extensively in Indonesia to meet the growing demand for teak wood. However, it is necessary to assess the conditions of teak stands throughout their growth period. The nondestructive testing of wood utilizing dielectric spectroscopy approaches based on electrical properties is currently under development, particularly for evaluating tree stands. This study aimed to analyze the dielectric values of fast-growing teak tree stands within a frequency range of 250 kHz to 60 MHz and to understand the relationship between their physical and anatomical properties. A capacitance measurement system was employed to collect dielectric spectroscopy data directly from trees aged 4, 5, and 7 years. Simultaneously, physical and anatomical samples were obtained using a 0.5 cm diameter increment borer. The results revealed significant differences in the fiber length, lumen diameter, and wall thickness at each age. The optimal dielectric frequency for distinguishing wood properties in standing trees was identified to be within a range of 18 MHz to 23 MHz. In the linear model, a moderate relationship was observed with a correlation coefficient of (r)0.403, although the coefficient of determination (r2) was weak at 0.162 for green density. However, a robust relationship was observed in the linear model for specific gravity with r = 0.826 and r2 = 0.682. A weak but significant relationship was also identified with r = 0.2, a coefficient of determination of r2 = 0.04, and a significance level < 0.05 in the predictive model of wood anatomy properties (vessel diameter and fiber wall thickness). Models with low r2 but high significance indicate that the independent variables still noticeably contribute to explaining the dependent variable. Further analysis and data processing can be enhanced by identifying the crucial variables in the capacitance measurement system.

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Section: Introductionmentioning

confidence: 99%

Electrical Properties at Multi-Frequencies for Analysis of Physical and Anatomical Properties of Fast-Growing Standing Teak Trees at Various Ages

Agustiningrum,

Siregar,

Damayanti

et al. 2024

Forests

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“…In current studies, representative ones are recurrent neural network (RNN), long- and short-term memory (LSTM), CNN, restricted Boltzmann machine (RBM), etc., among which CNN is one of the most widely used deep learning models in recent years [ 17 , 18 ]. Its main feature is that in the network model, the two layers are fully connected, and there is no connection within the layer.…”

Section: Introductionmentioning

confidence: 99%

Medical Data Classification Assisted by Machine Learning Strategy

Wang¹,

Zuo

2022

Computational and Mathematical Methods in Medicine

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With the development of science and technology, data plays an increasingly important role in our daily life. Therefore, much attention has been paid to the field of data mining. Data classification is the premise of data mining, and how well the data is classified directly affects the performance of subsequent models. In particular, in the medical field, data classification can help accurately determine the location of patients’ lesions and reduce the workload of doctors in the treatment process. However, medical data has the characteristics of high noise, strong correlation, and high data dimension, which brings great challenges to the traditional classification model. Therefore, it is very important to design an advanced model to improve the effect of medical data classification. In this context, this paper first introduces the structure and characteristics of the convolutional neural network (CNN) model and then demonstrates its unique advantages in medical data processing, especially in data classification. Secondly, we design a new kind of medical data classification model based on the CNN model. Finally, the simulation results show that the proposed method achieves higher classification accuracy with faster model convergence speed and the lower training error when compared with conventional machine leaning methods, which has demonstrated the effectiveness of the new method in respect to medical data classification.

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Automatic resin duct detection and measurement from wood core images using convolutional neural networks

Fabijańska

Cahalan

2023

Sci Rep

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The structure and features of resin ducts provide valuable information about environmental conditions accompanying the growth of trees in the genus Pinus. Therefore analysis of resin duct characteristics has been an increasingly common measurement in dendrochronology. However, the measurement is tedious and time-consuming since it requires thousands of ducts to be manually marked in an image of an enlarged wood surface. Although tools exist to automate some stages of this process, no tool exists to automatically recognize and analyze the resin ducts and standardize them with the tree rings they belong to. This study proposes a new fully automatic pipeline that quantifies the properties of resin ducts in terms of the tree ring area to which they belong. A convolutional neural network underlays the pipeline to detect resin ducts and tree-ring boundaries. Also, a region merging procedure is used to identify connected components corresponding to successive rings. Corresponding ducts and rings are next related to each other. The pipeline was tested on 74 wood images representing five Pinus species. Over 8000 tree-ring boundaries and almost 25,000 resin ducts were analyzed. The proposed method detects resin ducts with a sensitivity of 0.85 and precision of 0.76. The corresponding scores for tree-ring boundary detection are 0.92 and 0.99, respectively.

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Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

Cited by 7 publications

References 31 publications

Electrical Properties at Multi-Frequencies for Analysis of Physical and Anatomical Properties of Fast-Growing Standing Teak Trees at Various Ages

Electrical Properties at Multi-Frequencies for Analysis of Physical and Anatomical Properties of Fast-Growing Standing Teak Trees at Various Ages

Medical Data Classification Assisted by Machine Learning Strategy

Automatic resin duct detection and measurement from wood core images using convolutional neural networks

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