In the forestry industry tree trunks are currently classified manually. The object of this thesis is to answer whether it is possible to automate this using modern computer hardware and image-classification of tree-trunks using machine learning algorithms. The report concludes, based on results from controlled experiments that it is possible to achieve an accuracy above 90% across the genuses Birch, Pine and Spruce with a classification-time per tree shorter than 500 milli seconds. The report further compares these results against previous research and concludes that better results are probable.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-98698 |
| Date | January 2020 |
| Creators | Carlsson, David |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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