Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Using RGB images and machine learning to detect and classify Root and Butt-Rot (RBR) in stumps of Norway spruce
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-4600-8652
Umeå University, Faculty of Science and Technology, Department of Computing Science. Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431 Ås, Norway.ORCID iD: 0000-0003-0830-5303
Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO).
Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO).
Show others and affiliations
2019 (English)In: Forest Operations in Response to Environmental Challenges, 2019Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Root and butt-rot (RBR) has a significant impact on both the material and economic outcome of timber harvesting. An accurate recording of the presence of RBR during timber harvesting would enable a mapping of the location and extent of the problem, providing a basis for evaluating spread in a climate anticipated to enhance pathogenic growth in the future. Therefore, a system to automatically identify and detect the presence of RBR would constitute an important contribution in addressing the problem without increasing workload complexity for the machine operator. In this study we developed and evaluated an approach based on RGB images to automatically detect tree-stumps and classify them as to the absence or presence of rot. Furthermore, since knowledge of the extent of RBR is valuable in categorizing logs, we also classify stumps to three classes of infestation; rot = 0%, 0% < rot < 50% and rot ≥50%. We used deep learning approaches and conventional machine learning algorithms for detection and classification tasks. The results showed that tree-stumps were detected with precision rate of 95% and recall of 80%. Stumps without and with root and butt-rot were correctly classified with accuracy of 83.5% and 77.5%. Classifying rot into three classes resulted in 79.4%, 72.4% and 74.1% accuracy respectively. With some modifications, the algorithm developed could be used either during the harvesting operation to detect RBR regions on the tree-stumps or as a RBR detector for post-harvest assessment of tree-stumps and logs.

Place, publisher, year, edition, pages
2019.
National Category
Forest Science Robotics Signal Processing Computer Vision and Robotics (Autonomous Systems)
Research subject
computer and systems sciences
Identifiers
URN: urn:nbn:se:umu:diva-159977OAI: oai:DiVA.org:umu-159977DiVA, id: diva2:1322918
Conference
NB Nord Conference: Forest Operations in Response to Environmental Challenges, Honne, Norway, June 3-5, 2019.
Funder
The Research Council of Norway, NFR281140Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-25

Open Access in DiVA

fulltext(574 kB)14 downloads
File information
File name FULLTEXT01.pdfFile size 574 kBChecksum SHA-512
21530126ae5e77ef6c1d3ab190aa64049f2c702232dbb091ffe8c53948f2911fdeaae24a7666fde96800b47d0330b72afde4750faa877af3ebac890b2fae6a01
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Ringdahl, OlaOstovar, Ahmad
By organisation
Department of Computing Science
Forest ScienceRoboticsSignal ProcessingComputer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 14 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 161 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf