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Related Experiment Video

Updated: Jul 24, 2025

A Rapid Laser Probing Method Facilitates the Non-invasive and Contact-free Determination of Leaf Thermal Properties

A Rapid Laser Probing Method Facilitates the Non-invasive and Contact-free Determination of Leaf Thermal Properties

Published on: January 7, 2017

An automated method for stem diameter measurement based on laser module and deep learning.

Sheng Wang^1,2, Rao Li^1,2, Huan Li^1,2

¹School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China.

|July 5, 2023

Summary

This summary is machine-generated.

This study introduces an automated device for measuring tree stem diameter (SD), offering an efficient and accurate alternative to traditional methods. The novel approach ensures precise forest inventory data collection across diverse environments.

Keywords:

Deep learning Forest inventory Image sensor Laser module Measurement

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Area of Science:

Forestry Science
Remote Sensing Technology
Ecological Monitoring

Background:

Traditional stem diameter (SD) measurement is vital for forest management but is labor-intensive and costly.
Existing methods require expert personnel, leading to time delays and increased expenses.
There is a need for automated, efficient, and accurate SD measurement techniques.

Purpose of the Study:

To develop and validate a novel device and method for automatic stem diameter measurement.
To provide a cost-effective and portable solution for forest resource management.
To improve the efficiency and accuracy of SD data collection in natural environments.

Main Methods:

A device integrating an image sensor and a laser module was developed for automatic SD measurement.
A laser module projected a reference spot on the tree stem for calibration.
An end-to-end model identified the trunk contour from images, calculating SD based on contour and spot diameter.

Main Results:

The method demonstrated high consistency with field tape measurements across diverse scenarios (transitional woodland/shrub, mixed forest, urban areas).
Achieved an absolute mean error of 0.36 cm and a root mean square error of 0.45 cm for SD values ranging from 2.00 cm to 89.00 cm.
The integrated device is low-cost, portable, versatile, and performs with higher accuracy than many existing methods.

Conclusions:

The developed method enables automatic, efficient, and accurate stem diameter measurement in natural settings.
Future work includes integrating the device into autonomous mobile robots for expanded application.
This technology holds potential for revolutionizing forest inventory and management practices.