Automated classification of trees and evaluation of cone yield using drones

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.