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All Journal Journal of Innovation and Applied Technology AGROSAINSTEK: Jurnal Ilmu dan Teknologi Pertanian Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat MALLOMO: Journal of Community Service Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Jurnal Sains dan Teknologi Pertanian Perbal : Jurnal Pertanian Berkelanjutan Abdimas Langkanae Wanatani Journal of Applied Agricultural Science and Technology Perbal: Jurnal Pertanian Berkelanjutan Agrotech Journal JURNAL ABDI SAINS (JURNAL PENGABDIAN AKSI BERKELANJUTAN DAN INOVASI PEMBERDAYAAN MASYARAKAT)
Mutmainnah
Universitas Cokroaminoto Palopo
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Assessing the Effectiveness of Object-Based Image Analysis in Mapping High Visual Similarity Objects from Conventional Drone Imagery (Case Study: The Maturity Level of Sago) Iriansa Iriansa; Mutmainnah Mutmainnah; Masluki Masluki; Andi Jumardi; Ichwan Muis; Budi Utomo Putra Azis; Erwin Amri
Journal of Applied Agricultural Science and Technology Vol. 10 No. 3 (2026): Articles in Press
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v10i3.536

Abstract

Conventional Red-Green-Blue (RGB) unmanned aerial vehicle (UAV) imagery offers a cost-effective alternative for precision agriculture; however, its limited spectral separability constrains classification when target classes exhibit high visual similarity, particularly in non-cultivated areas with dense vegetation. Maturity assessment of sago palm (Metroxylon sagu Rottb.) exemplifies this challenge: identification of the harvestable stage remains dependent on labour-intensive field inspection, and delayed identification causes stem mortality and yield loss. This study evaluates an Object-Based Image Analysis (OBIA) framework for discriminating three sago maturity levels (Young, Harvestable, Overripe) from RGB orthomosaics and a Digital Surface Model over a 9-hectare non-cultivated site in Wailawi, North Luwu, Indonesia, using a DJI Phantom 4 Pro at 50 m altitude. Multi-resolution segmentation generated 6,210 crown-level objects, classified by Random Forest under two configurations: a five-feature set and a seventeen-feature set optimised through Recursive Feature Elimination. Evaluation used 600 independent objects (200 per class) from the testing partition through stratified random sampling, re-labelled by visual interpretation, with 95% confidence intervals from 1,000 bootstrap resamples. The seventeen-feature model outperformed the baseline, yielding Overall Accuracy of 93.00% (95% CI: 91.00–94.83) versus 89.00% (86.83–91.50), Macro-F1 of 92.92% versus 88.92%, and Cohen's Kappa of 0.895 (0.860–0.922) against 0.835 (0.795–0.873). Classification uncertainty concentrated at the Young-Harvestable boundary, whereas Overripe was consistently discriminated (F1 = 98.77%). Visible-band spectral statistics, GLCM and GLDV texture descriptors, and DSM-derived structural features contributed most to accuracy, while geometric descriptors showed marginal influence. The framework establishes a robust and economically accessible pathway for operational sago maturity monitoring.
Co-Authors Andi Jumardi Andi Jumardi Budi Utomo Putra Azis Busra Bumbungan, Busra Chrystover Rianto Pranoto D. Dandi Erwin Amri Feri Anggara Firdamayanti, Erni Fitri Gita Srihidayati Hairuddin, Rahman Hishar Mirsam Hishar Mirsam Ichwan Muis Iriansa Iriansa Iriansa Kiki Amalia Mariyam Mangkunegara Masluki, Masluki Muh. Arifin Ilham Muis, Ichwan Naim, Muhammad Naim, Muhammad Rahmawasiah Rahmawasiah Sri Hastuty Saruman Suhaeni Suhaeni Suhaeni Suhaeni, Suhaeni Ulfa Novita Sari Yoki Raditya