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All Journal Journal of Applied Agricultural Science and Technology Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences
Budi Utomo Putra Azis
Universitas Teknologi Sulawesi
Agriculture, Biological Sciences & Forestry Education Environmental Science Mathematics Physics Other

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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.
Analisis Perubahan Tata Guna Lahan Dan Kerentanan Ekosistem DAS Siwa, Sulawesi Selatan Budi Utomo Putra Azis; Iriansa Iriansa; Agsa Dewantara
Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences Vol. 5 No. 4 (2026): MIPA dan dan Pendidikan lingkup MIPA
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/venn.v5i4.582

Abstract

The Siwa Watershed (DAS Siwa) in South Sulawesi is a strategic ecosystem spanning Wajo, Sidrap, and Luwu Regencies. Uncontrolled land use changes in this area have the potential to reduce ecological carrying capacity and increase ecosystem vulnerability. This study aims to analyze land use changes and ecosystem vulnerability levels in DAS Siwa during the 2015–2025 period using a descriptive quantitative-spatial approach based on Geographic Information Systems (GIS). Data used include multitemporal land use imagery, rainfall data, topographic maps, and administrative boundaries. Analysis shows that the total area of DAS Siwa is 26,866.85 ha, dominated by forest (70.66%) but experiencing deforestation of 552.99 ha (24.37%) during the observation period. Mixed plantations increased significantly by 389.70 ha (17.18%), while settlements and agricultural land also expanded. Ecosystem vulnerability was assessed through a composite index considering land change, rainfall intensity, slope, and vegetation cover. Spatial overlay results identified high vulnerability zones in the central and upstream parts of the watershed, coinciding with active deforestation and high rainfall areas. These findings are expected to serve as a scientific basis for sustainable and ecosystem-based management of DAS Siwa.
Co-Authors Agsa Dewantara Andi Jumardi Erwin Amri Ichwan Muis Iriansa Iriansa Masluki, Masluki Mutmainnah