Evidence map›Paper›PMID 37408076›Full record

ArticlePlant methods2023

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

Sheng Wang, Rao Li, Huan Li, Xiaowen Ma, Qiang Ji, Fu Xu, Hongping Fu

Abstract read
In one paragraph

Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Sheng WangSchool of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China.
Rao LiSchool of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China.
Huan LiSchool of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China.
Xiaowen MaSchool of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China.
Qiang JiSpace Star Technology Co., Ltd., Beijing, 100048, China.
Fu XuSchool of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China.
Hongping FuSchool of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China. fhongping@bjfu.edu.cn.

Funding

Focus Tracking Project of Beijing Forestry University BLRD202124
6 · The paper itself

Abstract

backgroundMeasuring stem diameter (SD) is a crucial foundation for forest resource management, but current methods require expert personnel and are time-consuming and costly. In this study, we proposed a novel device and method for automatic SD measurement using an image sensor and a laser module. Firstly, the laser module generated a spot on the tree stem that could be used as reference information for measuring SD. Secondly, an end-to-end model was performed to identify the trunk contour in the panchromatic image from the image sensor. Finally, SD was calculated from the linear relationship between the trunk contour and the spot diameter in pixels.

resultsWe conducted SD measurements in three natural scenarios with different land cover types: transitional woodland/shrub, mixed forest, and green urban area. The SD values varied from 2.00 cm to 89.00 cm across these scenarios. Compared with the field tape measurements, the SD data measured by our method showed high consistency in different natural scenarios. The absolute mean error was 0.36 cm and the root mean square error was 0.45 cm. Our integrated device is low cost, portable, and without the assistance of a tripod. Compared to most studies, our method demonstrated better versatility and exhibited higher performance.

conclusionOur method achieved the automatic, efficient and accurate measurement of SD in natural scenarios. In the future, the device will be further explored to be integrated into autonomous mobile robots for more scenarios.

Indexed as

Deep learningForest inventoryImage sensorLaser moduleMeasurement

Identifiers

PMID37408076
PMCPMC10324111

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.