A transfer residual neural network based on ResNet-34 for detection of wood knot defects

M Gao, D Qi, H Mu, J Chen - Forests, 2021 - mdpi.com
In recent years, due to the shortage of timber resources, it has become necessary to reduce
the excessive consumption of forest resources. Non-destructive testing technology can …

Image segmentation-a survey of soft computing approaches

N Senthilkumaran, R Rajesh - 2009 international conference on …, 2009 - ieeexplore.ieee.org
Soft computing is an emerging field that consists of complementary elements of fuzzy logic,
neural computing and evolutionary computation. Soft computing techniques have found …

[图书][B] Nondestructive characterization and imaging of wood

V Bucur - 2003 - books.google.com
This book on the Nondestructive Characterization and Imaging of Wood by Professor
Voichita Bucur is truly the most outstanding reference on the subject ever written. Since the …

Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models

R Özçelik, MJ Diamantopoulou… - Forest ecology and …, 2013 - Elsevier
Artificial neural network models offer a number of advantages including the ability to
implicitly detect complex nonlinear relationships between input and output variables, which …

Artificial neural networks as an alternative tool in pine bark volume estimation

MJ Diamantopoulou - Computers and electronics in agriculture, 2005 - Elsevier
The utilization of pine trees bark is receiving increasing attention. For management
purposes, it is very useful to be able to estimate bark volume quantity of standing trees. This …

A Novel Deep Convolutional Neural Network Based on ResNet‐18 and Transfer Learning for Detection of Wood Knot Defects

M Gao, P Song, F Wang, J Liu, A Mandelis… - Journal of …, 2021 - Wiley Online Library
Wood defects are quickly identified from an optical image based on deep learning
methodology, which effectively improves wood utilization. Traditional neural network …

[PDF][PDF] Application of artificial neural networks in image recognition and classification of crop and weeds

CC Yang, SO Prasher, JA Landry… - Canadian agricultural …, 2000 - academia.edu
Eng. 42: 147-152. The objective of this study was to develop a backpropagation artificial
neural network (ANN) model that could distinguish young corn plants from weeds. Although …

Automated grading and defect detection: A review.

DT Pham, RJ Alcock - Forest Products Journal, 1998 - search.ebscohost.com
In a plant that manufactures wood products, such as lumber, dimension stock, or veneer
sheets, inspection of the products is a necessary part of the production process. At present …

RETRACTED: Artificial neural network for defect detection in CT images of wood

L Pan, R Rogulin, S Kondrashev - 2021 - Elsevier
After a thorough investigation, the Editors-in-Chief have concluded that the acceptance of
this article was partly based upon the positive advice of at least one illegitimate reviewer …

Use of genetic artificial neural networks and spectral imaging for defect detection on cherries

D Guyer, X Yang - Computers and electronics in agriculture, 2000 - Elsevier
A machine vision system was created to identify different types of tissue characteristics on
cherries. It consists of an enhanced NIR range vidicon black and white camera (sensing …