Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Sung-Wook Hwang,Junji Sugiyama

doi:10.1186/s13007-021-00746-1

Abstract

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

Highlights

In wood identification based on conventional machine learning (ML), different feature types are selected depending on the type of image and classification problem, most of them are for texture and local features
Computer vision (CV)-based wood identification continues to evolve in the development of on-site wood identification systems that enable consistent judgment without human prejudice
deep learning (DL) in recent years has provided a technical foundation for more accurate wood identification and is expected to answer a variety of questions in wood anatomy shortly

Summary

Introduction

Every tree has clues that can help with its identification. Leaves, needles, barks, fruits, flowers, and twigs are important features for tree identification. The classifier learns the features extracted from the training set images and their labels to build a classification model. In CV-based wood identification, it is desirable to split a dataset by individual units, not by images, because the splitting process is important in determining the reliability of classification models.

Results

Conclusion