| Issue |
E3S Web Conf.
Volume 623, 2025
IV International Conference on Ensuring Sustainable Development: Ecology, Earth Science, Energy and Agriculture (AEES2024)
|
|
|---|---|---|
| Article Number | 04015 | |
| Number of page(s) | 9 | |
| Section | Current Agricultural Development | |
| DOI | https://doi.org/10.1051/e3sconf/202562304015 | |
| Published online | 08 April 2025 | |
Automated classification of trees and evaluation of cone yield using drones
Siberian State University of Science and Technology named after Academician M.F. Reshetnev, building 31, Prospekt imeni gazety Krasnoyarsky Rabochy, Krasnoyarsk, Krasnoyarsk Krai, 660037, Russia
* Corresponding author: dumooroo@gmail.com
In the study, analyzed the effectiveness of unmanned aerial vehicles (UAVs) for forest monitoring. Investigated various image processing algorithms aimed at identifying tree species. Revealed the potential of machine learning techniques in automating data analysis. Established a methodology that combines computer vision with UAV imagery for enhanced accuracy. Proposed new approaches for assessing the number of cones on trees, which can significantly improve forest resource management practices.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.