SkyPlanter: Aerial Reforestation With an Ultralight, Seedling–Planting Drone

This article presents SkyPlanter, the first drone-based system for aerial reforestation with tree seedlings. Traditional tree planting is labor-intensive, physically demanding, and expensive—making it ideal for automation. Current mechanized solutions depend on large, heavy, ground-based excavator-based solutions best suited for extensive clear-cuts, but which struggle on steep or uneven terrain, and carry prohibitive relocation costs for smaller operations. SkyPlanter is a drone-mounted seedling-planting system that enables it to easily traverse rugged or steep terrain while remaining inexpensive, easily transported, and highly scalable. It uses an ultra-lightweight compressed air planting mechanism that inserts seedlings and compacts the surrounding soil. Its innovative double-telescoping design reduces vehicle weight to 15.2 kg (without batteries) or 16.4–20.8 kg (with batteries, depending on flight duration). This article details the system’s novel planting and ground compression mechanisms, its unique high-pressure pneumatic power systems, and its custom quadrotor carrier drone. We demonstrate its feasibility in the first-ever aerial seedling-planting tests in a forest environment. The system is proposed as a cost-effective, scalable reforestation solution with high automation potential.

Authors

Institutions

Publication Details

Journal
IEEE/ASME Transactions on Mechatronics
Published
2025-01-01
DOI
https://doi.org/10.1109/tmech.2025.3606620
Citations
1
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.80
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SkyPlanter: Aerial Reforestation With an Ultralight, Seedling–Planting Drone

Steffan Lloyd, Emad Ebeid, Rasmus Astrup, Thor Kamp Opstrup
1 citations
IEEE/ASME Transactions on Mechatronics
Remote Sensing and LiDAR Applications
0.80
article

SkyPlanter: Aerial Reforestation With an Ultralight, Seedling–Planting Drone

Steffan Lloyd, Emad Ebeid, Rasmus Astrup, Thor Kamp Opstrup
article en
1 citations

Abstract

This article presents SkyPlanter, the first drone-based system for aerial reforestation with tree seedlings. Traditional tree planting is labor-intensive, physically demanding, and expensive—making it ideal for automation. Current mechanized solutions depend on large, heavy, ground-based excavator-based solutions best suited for extensive clear-cuts, but which struggle on steep or uneven terrain, and carry prohibitive relocation costs for smaller operations. SkyPlanter is a drone-mounted seedling-planting system that enables it to easily traverse rugged or steep terrain while remaining inexpensive, easily transported, and highly scalable. It uses an ultra-lightweight compressed air planting mechanism that inserts seedlings and compacts the surrounding soil. Its innovative double-telescoping design reduces vehicle weight to 15.2 kg (without batteries) or 16.4–20.8 kg (with batteries, depending on flight duration). This article details the system’s novel planting and ground compression mechanisms, its unique high-pressure pneumatic power systems, and its custom quadrotor carrier drone. We demonstrate its feasibility in the first-ever aerial seedling-planting tests in a forest environment. The system is proposed as a cost-effective, scalable reforestation solution with high automation potential.

IEEE/ASME Transactions on Mechatronics
University of Southern Denmark (DK), Norwegian Institute of Bioeconomy Research (NO)
Openalex Percentile: Top 24%
Remote Sensing and LiDAR Applications
0.80
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.