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Using RGB images and machine learning to detect and classify Root and Butt-Rot (RBR) in stumps of Norway spruce
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. 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).
Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO).
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2019 (Engelska)Ingår i: Forest Operations in Response to Environmental Challenges: Proceedings of the Nordic-Baltic Conference on Operational Research (NB-NORD), June 3-5, Honne, Norway / [ed] Simon Berg & Bruce Talbot, Norsk institutt for bioøkonomi (NIBIO) , 2019Konferensbidrag, Muntlig presentation med publicerat abstract (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Norsk institutt for bioøkonomi (NIBIO) , 2019.
Serie
NIBIO Bok, E-ISSN 2464‐1189 ; 5(6)2019
Nationell ämneskategori
Skogsvetenskap Robotteknik och automation Signalbehandling Datorseende och robotik (autonoma system)
Forskningsämne
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:umu:diva-159977ISBN: 978-82-17-02339-5 (digital)OAI: oai:DiVA.org:umu-159977DiVA, id: diva2:1322918
Konferens
NB Nord Conference: Forest Operations in Response to Environmental Challenges, Honne, Norway, June 3-5, 2019.
Forskningsfinansiär
Norges forskningsråd, NFR281140Tillgänglig från: 2019-06-11 Skapad: 2019-06-11 Senast uppdaterad: 2020-02-05Bibliografiskt granskad

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Ostovar, AhmadRingdahl, Ola

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SkogsvetenskapRobotteknik och automationSignalbehandlingDatorseende och robotik (autonoma system)

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