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Detection and Classification of Root and Butt-Rot (RBR) in Stumps of Norway Spruce Using RGB Images and Machine Learning

Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431 Ås, Norway
Department of Computing Science, Umeå University, 901 87 Umeå, Sweden
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1579;
Received: 15 February 2019 / Revised: 26 March 2019 / Accepted: 28 March 2019 / Published: 1 April 2019
(This article belongs to the Special Issue Agricultural Sensing and Image Analysis)
PDF [6323 KB, uploaded 5 April 2019]


Root and butt-rot (RBR) has a significant impact on both the material and economic outcome of timber harvesting, and therewith on the individual forest owner and collectively on the forest and wood processing industries. 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 to 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 into three classes of infestation; rot = 0%, 0% < rot < 50% and rot ≥ 50%. In this work 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%. Using only the correct output (TP) of the stump detector, stumps without and with RBR were correctly classified with accuracy of 83.5% and 77.5%, respectively. Classifying rot into three classes resulted in 79.4%, 72.4%, and 74.1% accuracy for stumps with rot = 0%, 0% < rot < 50%, and rot ≥ 50%, respectively. With some modifications, the developed algorithm could be used either during the harvesting operation to detect RBR regions on the tree stumps or as an RBR detector for post-harvest assessment of tree stumps and logs. View Full-Text
Keywords: deep learning; forest harvesting; tree stumps; automatic detection and classification deep learning; forest harvesting; tree stumps; automatic detection and classification

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Ostovar, A.; Talbot, B.; Puliti, S.; Astrup, R.; Ringdahl, O. Detection and Classification of Root and Butt-Rot (RBR) in Stumps of Norway Spruce Using RGB Images and Machine Learning. Sensors 2019, 19, 1579.

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