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The system includes three main modules: real-time state estimation, map optimization with individual tree detection, and path planning. All computations are performed onboard the UAV using a single-board computer. An IMU, a LiDAR, and optionally a barometer are the only sensors used in the proposed system. A pose-graph approach fuses data from these sensors in real time to estimate the vehicle\u2019s state. Keyframes are selected from LiDAR scans, and individual trees are segmented using a region-growing algorithm. The keyframes are also used to generate loop-closure candidates, which help reduce long-term drift in state estimation. A path-planning framework uses detected seedling trees to plan a path from the top of one tree to the top of another, while avoiding surrounding obstacles. The proposed framework is tested in real-world forest environments, demonstrating accurate tree segmentation, coherent mapping, precise state estimation, and autonomous navigation for spraying.<\/jats:p>","DOI":"10.1007\/s10846-026-02375-z","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:53:37Z","timestamp":1772780017000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Autonomous UAV System for Precision Spraying of Seedling Pines to Prevent Moose Damage"],"prefix":"10.1007","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1589-8541","authenticated-orcid":false,"given":"Issouf","family":"Ouattara","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arto","family":"Visala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"2375_CR1","doi-asserted-by":"publisher","unstructured":"Nevalainen, S., Matala, J., Korhonen, K., Ihalainen, A., Nikula, A.: Moose damage in national forest inventories (1986\u20132008) in Finland. 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