International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Smart Forest Monitoring: AI-based Tree Counting and Identification |
|---|---|
| ABSTRACT | Despite their critical ecological and economic value, forests remain highly vulnerable to illegal logging and depletion of endangered species. Traditional manual surveillance is often slow, labor-intensive, and insufficient for covering vast areas. This study introduces an automated forest monitoring framework that leverages unmanned aerial vehicles (UAVs) and deep learning. By analyzing aerial top-view imagery, our system was trained to detect, classify, and map specific high-value and medicinal tree species that are frequently targeted by smugglers. The proposed model provides accurate tree counts and distribution analytics, significantly reducing reliance on manual ground surveys. This technology offers forest departments a scalable tool for data-driven decision-making, ensuring timely intervention against illegal activities and promoting effective biodiversity conservation in the future. |
| AUTHOR | DR. INDIRA SP, DHANUSH KUMAR PV, H TARUN, GANESH V, NITIN R ALAGAWADI Associate Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312108 |
| pdf/108_Smart Forest Monitoring AI-based Tree Counting and Identification.pdf | |
| KEYWORDS | |
| References | 1. P. Kovačovič, R. Pirník, and J. Kafková, “Satellite-Based Forest Stand Detection Using Artificial Intelligence,” IEEE Access, 2025. 2. Md Jelas, M. A. Zulkifley, M. Abdullah, and M. Spraggon, “Deforestation detection using deep learning-based semantic segmentation techniques: a systematic review,” Frontiers in Forests and Global Change, 2024. 3. Y. Huang, B. Ou, K. Meng, and B. Yang, “Tree Species Classification from UAV Canopy Images with Deep Learning Models,” IEEE Xplore / ScienceDirect, 2024. 4. L. Wang, R. Zhang, L. Zhang, T. Yi, and D. Zhang, “Research on Individual Tree Canopy Segmentation of Camellia oleifera Based on a UAV-LiDAR System,” 2024. 5. A.Venkata Sai, Abhishek, and S. Kotni, “Detectron2 Object Detection & Manipulating Images using Cartoonization,” International Journal of Engineering Research & Technology (IJERT), vol. 10, 2021. |
