A computer-aided method for identifying the presence of softwood growth ring boundaries.

Qizhao Lin,Yongke Sun,Jian Qiu,Tuo He,Xin He,Sen Xiang

doi:10.1371/journal.pone.0235727

Abstract

The objective of this study was to develop a computer-aided method to quantify the obvious degree of growth ring boundaries of softwood species, based on data analysis with some image processing technologies. For this purpose, a 5× magnified cross-section color micro-image of softwood was cropped into 20 sub-images, and then every image was binarized as a gray image according to an automatic threshold value. After that, the number of black pixels in the gray image was counted row by row and the number of black pixels was binarized to 0 or 100. Finally, a transition band from earlywood to latewood on the sub-image was identified. If everything goes as planned, the growth ring boundaries of the sub-image would be distinct. Otherwise would be indistinct or absent. If more than 50% sub-images are distinct, with the majority voting method, the growth ring boundaries of softwood would be distinct, otherwise would be indistinct or absent. The proposed method has been visualized as a growth-ring-boundary detecting system based on the .NET Framework. A sample of 100 micro-images (see S1 Fig via https://github.com/senly2019/Lin-Qizhao/) of softwood cross-sections were selected for evaluation purposes. In short, this detecting system computes the obvious degree of growth ring boundaries of softwood species by image processing involving image importing, image cropping, image reading, image grayscale, image binarization, data analysis. The results showed that the method used avoided mistakes made by the manual comparison method of identifying the presence of growth ring boundaries, and it has a high accuracy of 98%.

Highlights

A wood species can be identified according to the macroscopic and microscopic structural characteristics of the wood, which is a time-consuming process
This detecting system computes the obvious degree of growth ring boundaries of softwood species by image processing involving image importing, image cropping, image reading, image grayscale, image binarization, data analysis
Compared with the traditional method [17], to judge whether the growth ring boundaries were distinct, the advantage of the proposed method is providing a qualitative conclusion with the majority voting method based on quantitative computation, which minimized mistakes made by the manual comparison method

Summary

Introduction

A wood species can be identified according to the macroscopic and microscopic structural characteristics of the wood, which is a time-consuming process. The traditional methods of wood identification include manual comparison, dichotomous keys, multiple entry keys, punch card search and computer database program search [1]. The phenomenon of distinct, indistinct or absent growth rings is a wide range of tree-ring research, and its feature is used for wood identification [7]. Researchers have attempted to recognize wood species by utilizing a growth ring boundary detection algorithm [8] such as the Gray Level Co-occurrence Matrix [8, 9], and the color histogram statistical method [10] to extract wood features. Various techniques, including Support Vector Machine (SVM) [11], K-nearest neighbor (KNN) ([12,13,14]), and neural network [15,16], have been used to create many classifiers

Objectives

Methods

Results

Conclusion