COMPARITIVE STUDY OF TREE COUNTING ALGORITHMS IN DENSE AND SPARSE VEGETATIVE REGIONS

6 citations as recorded by crossref.

1. Toward a comprehensive model for estimating diameter at breast height of Japanese cypress (Chamaecyparis obtusa) using crown size derived from unmanned aerial systems K. Iizuka et al. 10.1016/j.compag.2021.106579
2. Explainable identification and mapping of trees using UAV RGB image and deep learning M. Onishi & T. Ise 10.1038/s41598-020-79653-9
3. Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India K. Shivaprakash et al. 10.3390/su14127154
4. Individual tree detection and counting based on high-resolution imagery and the canopy height model data Y. Zhang et al. 10.1080/10095020.2023.2299146
5. A Deep Neural Network for the Estimation of Tree Density Based on High-Spatial Resolution Image T. Liu et al. 10.1109/TGRS.2021.3101056
6. Tree Seedlings Detection and Counting Using a Deep Learning Algorithm D. Moharram et al. 10.3390/app13020895