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Presently, one of the major biotic stress agents in Europe is the European spruce bark beetle (Ips typographus L.) which is increasingly causing widespread tree mortality in northern latitudes as a consequence of the warming climate. Remote sensing using unoccupied aerial systems (UAS) together with evolving machine learning techniques provide a powerful tool for fast-response monitoring of forest health. The aim of this study was to investigate the performance of a deep one-stage object detection neural network in the detection of damage by I. typographus in Norway spruce trees using UAS RGB images. A Scaled-YOLOv4 (You Only Look Once) network was implemented and trained for tree health analysis. Datasets for model training were collected during 2013\u20132020 from three different areas, using four different RGB cameras, and under varying weather conditions. Different model training options were evaluated, including two different symptom rules, different partitions of the dataset, fine-tuning, and hyperparameter optimization. Our study showed that the network was able to detect and classify spruce trees that had visually separable crown symptoms, but it failed to separate spruce trees with stem symptoms and a green crown from healthy spruce trees. For the best model, the overall F-score was 89%, and the F-scores for the healthy, infested, and dead trees were 90%, 79%, and 98%, respectively. The method adapted well to the diverse dataset, and the processing results with different options were consistent. The results indicated that the proposed method could enable implementation of low-cost tools for management of I. typographus outbreaks.<\/jats:p>","DOI":"10.3390\/rs14246257","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T04:34:20Z","timestamp":1670819660000},"page":"6257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Heini","family":"Kanerva","sequence":"first","affiliation":[{"name":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7236-2145","authenticated-orcid":false,"given":"Eija","family":"Honkavaara","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5823-8180","authenticated-orcid":false,"given":"Roope","family":"N\u00e4si","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-4582","authenticated-orcid":false,"given":"Teemu","family":"Hakala","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8276-9259","authenticated-orcid":false,"given":"Samuli","family":"Junttila","sequence":"additional","affiliation":[{"name":"School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80100 Joensuu, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7941-4457","authenticated-orcid":false,"given":"Kirsi","family":"Karila","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6307-1637","authenticated-orcid":false,"given":"Niko","family":"Koivum\u00e4ki","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5308-8259","authenticated-orcid":false,"given":"Raquel","family":"Alves Oliveira","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7477-5357","authenticated-orcid":false,"given":"Mikko","family":"Pelto-Arvo","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5129-7364","authenticated-orcid":false,"given":"Ilkka","family":"P\u00f6l\u00f6nen","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, 40100 Jyv\u00e4skyl\u00e4, Finland"}]},{"given":"Johanna","family":"Tuviala","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland"}]},{"given":"Madeleine","family":"\u00d6stersund","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, 02150 Espoo, Finland"}]},{"given":"P\u00e4ivi","family":"Lyytik\u00e4inen-Saarenmaa","sequence":"additional","affiliation":[{"name":"School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80100 Joensuu, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/afe.12040","article-title":"Distribution of Norway Spruce Bark and Wood-Boring Beetles along Alpine Elevational Gradients: Norway Spruce Bark and Wood Beetles along Altitude","volume":"16","author":"Chinellato","year":"2014","journal-title":"Agr. Forest Entomol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s40725-021-00142-x","article-title":"Bark Beetle Outbreaks in Europe: State of Knowledge and Ways Forward for Management","volume":"7","author":"Krokene","year":"2021","journal-title":"Curr. For. 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