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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science MONSU'ANI TANO Jurnal Pengabdian Masyarakat Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Journal of System and Computer Engineering Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal Pengabdian Masyarakat Bangsa
Muh. Agus
Computer Science, Institut Teknologi Bacharuddin Jusuf Habibie, Parepare City, Indonesia
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Integration of YOLOv8 and IoT-Based LiDAR Sensors on Drones for Deforestation Area Detection and Reforestation Mapping Muh. Agus; Putri Ayu Maharani; Ilham Ali Marka M; Muhammad Aslam Al-Fadillah
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.11778

Abstract

Deforestation has caused significant ecosystem degradation and increased the need for more accurate and efficient monitoring systems and reforestation planning. This study develops an integrated system combining YOLOv8 and Internet of Things (IoT)-based LiDAR sensors on a drone platform to detect deforested areas and generate 3D mapping to support reforestation efforts. The dataset consists of 1,822 aerial images collected from public datasets and drone-mounted cameras under various lighting conditions and flight altitudes. The YOLOv8 model was trained using transfer learning with an input size of 640 × 640, a batch size of 16, a learning rate of 0.001, and 100 training epochs. The results demonstrate that the model achieved a precision of 93%, a recall of 90%, and an mAP@0.5 of 94%, while successfully performing real-time deforestation area detection on the drone platform. Integration with the LiDAR sensor produced 3D point cloud visualizations with mapping deviations of less than 2 meters. The developed system effectively supports the identification of priority areas for reforestation more rapidly, accurately, and efficiently than conventional manual survey methods.
Co-Authors Akhsa, Alvian Tri Putra Darti Alvian Tri Putra Darti Akhsa Amar, Muh. Ikhsan Andi Muhammad Bahrul Ulum Armansyah, Muhammad Dicky Bahar, Nur Aini Sri Oktaviani Eka Qadri Nuranti Elisabeth Tri Juliana Kandakon Gunadin, Indar Chaerah Hasyim, Muhammad Fadel Ilham Ali Marka M Indar Chaerah Gunadin Jusuf, Abd Karim Kandi, Damar M. Fauzan Iskandar Maharani, Putri Ayu Malika, Saphira Nur Mardhiyyah Rafrin Mardhiyyah Rafrin Mardhiyyah Rafrin, S.T., M.Sc. Marka M, Ilham Ali Muh. Abubakar Tunru Muh. Ikhsan Amar, S.Kom., M.Kom. Muhammad Aldi Alfatih Muhammad Aslam Al-Fadillah Muhammad Fadel Hasyim Muhammad Faruq Al-Fauzi S. Muhammad Irsyad Erlangga Muhammad Rivaldi Jefri Naili Suri Intizhami Naili Suri Intizhami, S.Kom., M.Kom. Nur Inaya Bahar Nuranti B, Eka Qadri Osama Iyad Al Ghozy Prof. Dr. Ir. Indar Chaerah Gunadin, ST.,MT.,IPM Putri Ayu Maharani Putri Ayu Maharani Putri Ayu Maharani, S.T., M.Sc. Rafdah Pritama Saputri Rafrin, Mardhiyyah Risnawati Rosmiati S., St. Aisyah Sitti Rahma Yunus