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)

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Published Jun 20, 2026
https://doi.org/10.55043/jaast.v10i3.536
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Vol. 10 No. 3 (2026): Articles in Press

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iriansa iriansa
a:1:{s:5:"en_US";s:31:"Universitas Cokroaminoto Palopo";}
Mutmainnah
Universitas Cokroaminoto Palopo
https://orcid.org/0009-0004-0351-5533
Masluki
Universitas Cokroaminoto Palopo
https://orcid.org/0000-0002-2168-8823
Andi Junardi
Universitas Cokroaminoto Palopo
Budi Utomo Putra Azis
Universitas Teknologi Sulawesi

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.

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iriansa iriansa, Mutmainnah, Masluki, Andi Junardi, Budi Utomo Putra Azis. 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). J. appl. agricultural sci. technol. [Internet]. 2026Jun.20 [cited 2026Jun.21];10(3):414-38. Available from: https://www.jaast.org/index.php/jaast/article/view/536

References

  1. Dewayani W, Suryani, Arum RH, Septianti E. Potential of sago products supporting local food security in South Sulawesi. IOP Conf Ser Earth Environ Sci 2022;974:012114. https://doi.org/10.1088/1755-1315/974/1/012114.
  2. Sidiq FF, Coles D, Hubbard C, Clark B, Frewer LJ. Sago and the indigenous peoples of Papua, Indonesia: A review. Journal of Agriculture and Applied Biology 2021;2:138–49. https://doi.org/10.11594/JAAB.02.02.08.
  3. Yusuf DN, Sutariati GAK. The potential of sago as a local food ingredient to support the food security in South Konawe. IOP Conf Ser Earth Environ Sci 2021;807:022077. 10.1088/1755-1315/807/2/022077
  4. Djoefrie MHB, Pembayun P, Baka LR. Sago Production Potential in Several Regions in Indonesia. The 14th International Sago Symposium SAGO 2023 TOKYO, 2024;23. https://www.sagopalmsociety.com/_files/ugd/3f58e5_398fcb790b40418ab832df78c76a3039.pdf#page=28
  5. Puspantari W, Cahyana PT, Saepudin A, Budiyanto B, Kurniasari I, Kusumasmarawati AD, et al. Sago Rice as an Environmentally Sustainable Food. BIO Web Conf EDP Sciences 2023;69:03012. https://doi.org/10.1051/bioconf/20236903012
  6. Fetriyuna F, Letsoin SMA, Jati IRAP, Purwestri RC. Potential of Underutilized Sago for Bioenergy Uses. Int J Adv Sci Eng Inf Technol 2024;14:144–50. https://doi.org/10.18517/IJASEIT.14.1.19202.
  7. Tampubolon AP, Turjaman M, Osaki M. Sago Palm Practice as Natural AeroHydro Culture. Tropical Peatland Eco-Management 2021:363–77. https://doi.org/10.1007/978-981-33-4654-3_12/COVER.
  8. Yumeina D, Kasmira K, Makkarennu M. Effect of heat moisture treatment on psychochemical modification of sago starch. AIP Conf Proc AIP Publishing 2023;2596. https://doi.org/10.1063/5.0119495
  9. Lim LWK, Chung HH, Hussain H, Bujang K. Sago Palm (Metroxylon sagu Rottb.): Now and Beyond. Pertanika J Trop Agric Sci 2019;42:435–51. https://www.researchgate.net/publication/333650292_Sago_Palm_Metroxylon_sagu_Rottb_Now_and_Beyond
  10. Syafiuddin M, Zubair H, Jayadi M, Baharuddin B, Busthanul N. The potential for developing sago-taro intercropping in South Sulawesi: a review. IOP Conf Ser Earth Environ Sci 2021;681:012039. https://doi.org/10.1088/1755-1315/681/1/012039.
  11. Wahed Z, Joseph A, Zen H, Kipli K. Sago Palm Detection and its Maturity Identification Based on Improved Convolution Neural Network. Pertanika J Sci Technol 2022;30:1219–36. https://doi.org/10.47836/PJST.30.2.20.
  12. Markus Rawung JB, Indrasti R. The Constraints to Sago Development and Improvement Efforts in Siau Tagulandang Biaro (Sitaro) Islands. E3S Web of Conferences 2021;232:01029. https://doi.org/10.1051/E3SCONF/202123201029.
  13. Nurlette AR, Mukson, Sumekar W. Sustainable Management of Sago (Metroxylon Spp) Agroindustry in East Indonesia. The International Journal of Social Sciences World (TIJOSSW) 2021;3:33–45. https://doi.org/10.5281/zenodo.5131391.
  14. Ellen R. How Nuaulu sago palms feature in debates around the measurement of plant use and valuation. The Cultural Value of Trees Routledge 2022:177–90. https://doi.org/10.4324/9780429320897
  15. Pue AG, Fletcher MT, Blaney B, Greenhill AR, Warner JM, Latifa A, et al. Addressing food insecurity in Papua New Guinea through food safety and sago cropping. Sago Palm: Multiple Contributions to Food Security and Sustainable Livelihoods 2018:123–37. https://doi.org/10.1007/978-981-10-5269-9_9
  16. Konuma H. Status and outlook of global food security and the role of underutilized food resources: Sago palm. Sago Palm: Multiple Contributions to Food Security and Sustainable Livelihoods 2018:3–16. https://doi.org/10.1007/978-981-10-5269-9_1/FIGURES/7
  17. Sabir RM, Mehmood K, Sarwar A, Safdar M, Muhammad NE, Gul N, et al. Remote sensing and precision agriculture: a sustainable future. Transforming agricultural management for a sustainable future: climate change and machine learning perspectives, Springer 2024;75–103. https://doi.org/10.1007/978-3-031-63430-7_4
  18. Guebsi R, Mami S, Chokmani K. Drones in precision agriculture: A comprehensive review of applications, technologies, and challenges. Drones 2024;8:686. https://doi.org/10.3390/drones8110686
  19. Kar D, Dhal SB. Advancing food security through drone-based hyperspectral imaging: applications in precision agriculture and post-harvest management. Environ Monit Assess 2025;197:283. https://doi.org/10.1007/s10661-025-13650-1
  20. Yang Q, Shi L, Han J, Yu J, Huang K. A near real-time deep learning approach for detecting rice phenology based on UAV images. Agric For Meteorol 2020;287:107938. https://doi.org/10.1 016/j.agrformet.2020.107938
  21. Tatsumi K, Igarashi N, Mengxue X. Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery. Plant Methods 2021;17:1–17. https://doi.org/10.1186/s13007-021-00761-2
  22. Plata IT, Panganiban EB, Alado DB, Taracatac AC, Bartolome BB, Labuanan FRE. Drone-based geographical information system (GIS) mapping of cassava pythoplasma disease (CPD) for precision agriculture. Int J Emerg Technol Adv Eng 2022;12:1–9. https://www.researchgate.net/publication/358377530_Drone-based_Geographical_Information_System_GIS_Mapping_of_Cassava_Pythoplasma_Disease_CPD_for_Precision_Agriculture
  23. Ojo IA, Costa L, Ampatzidis Y, Alferez F, Shukla S. Citrus fruit maturity prediction utilizing UAV multispectral imaging and machine learning. 2021 ASABE Annual International Virtual Meeting, American Society of Agricultural and Biological Engineers; 2021;1. https://doi.org/10.13031/aim.202100495
  24. Cheng M-F, Mukundan A, Karmakar R, Valappil MAE, Jouhar J, Wang H-C. Modern Trends and Recent Applications of Hyperspectral Imaging: A Review. Technologies (Basel) 2025;13:170. https://doi.org/10.3390/technologies13050170
  25. Sohl MA, Mahmood SA, Rasheed MU. Comparative performance of four machine learning models for land cover classification in a low-cost UAV ultra-high-resolution RGB-only orthomosaic. Earth Sci Inform 2024;17:2869–85. https://doi.org/10.1007/s12145-024-01318-2
  26. Šupčík A, Oršulová V. Using OBIA for detection of canopy and row-gap by using drone images in vineyards. Precision agriculture’21, Wageningen Academic Publishers; 2021, p. 531–7. https://doi.org/10.3920/978-90-8686-916-9_82
  27. Ventura D, Napoleone F, Cannucci S, Alleaume S, Valentini E, Casoli E, et al. Integrating low-altitude drone based-imagery and OBIA for mapping and manage semi natural grassland habitats. J Environ Manage 2022;321:115723. https://doi.org/10.1016/j.jenvman.2022.115723
  28. Ye Z, Yang K, Lin Y, Guo S, Sun Y, Chen X, et al. A comparison between Pixel-based deep learning and Object-based image analysis (OBIA) for individual detection of cabbage plants based on UAV Visible-light images. Comput Electron Agric 2023;209:107822. https://doi.org/10.1016/j.compag.2023.107822
  29. Deur M, Gašparović M, Balenović I. An evaluation of pixel-and object-based tree species classification in mixed deciduous forests using pansharpened very high spatial resolution satellite imagery. Remote Sens (Basel) 2021;13:1868. https://doi.org/10.3390/rs13101868
  30. Du B, Mao D, Wang Z, Qiu Z, Yan H, Feng K, et al. Mapping wetland plant communities using unmanned aerial vehicle hyperspectral imagery by comparing object/pixel-based classifications combining multiple machine-learning algorithms. IEEE J Sel Top Appl Earth Obs Remote Sens 2021;14:8249–58. 10.1109/JSTARS.2021.3100923
  31. Chen J, Chen Z, Huang R, You H, Han X, Yue T, et al. The Effects of Spatial Resolution and Resampling on the Classification Accuracy of Wetland Vegetation Species and Ground Objects: A Study Based on High Spatial Resolution UAV Images. Drones 2023;7:61. https://doi.org/10.3390/drones7010061
  32. Zhou R, Yang C, Li E, Cai X, Yang J, Xia Y. Object-based wetland vegetation classification using multi-feature selection of unoccupied aerial vehicle RGB imagery. Remote Sens (Basel) 2021;13:4910. https://doi.org/10.3390/rs13234910
  33. Iyoob AL, Norbert SA, Ameer MLF, Nasar-u-Minallah M, Zahir ILM, Nuskiya MHF. Using UAV RGB Image for Object-based Feature Detection. Procedia Comput Sci 2025;260:1052–9. https://doi.org/10.1016/j.procs.2025.03.290
  34. Yang D, Zhou N, Zhu Z, Ge H, Wang W, Xu C, et al. Coastal wetland classification method based on UAV imagery: integrating hierarchical sample enhancement and multiscale sample selection techniques. Geomatics, Natural Hazards and Risk 2025;16:2585167. https://doi.org/10.1080/19475705.2025.2585167
  35. Dietenberger S, Mueller MM, Stöcker B, Dubois C, Arlaud H, Adam M, et al. Accurate Mapping of Downed Deadwood in a Dense Deciduous Forest Using UAV-SfM Data and Deep Learning. Remote Sens (Basel) 2025;17:1610. https://doi.org/10.3390/rs17091610
  36. Ameslek O, Zahir H, Latifi H, Bachaoui EM. Combining OBIA, CNN, and UAV imagery for automated detection and mapping of individual olive trees. Smart Agricultural Technology 2024;9:100546. https://doi.org/10.1016/j.atech.2024.100546
  37. Kelly M, Feirer S, Hogan S, Lyons A, Lin F, Jacygrad E. Mapping orchard trees from UAV imagery through one growing season: A comparison between OBIA-based and three CNN-based object detection methods. Drones 2025;9:593. https://doi.org/10.3390/drones9090593
  38. Iriansa, Mutmainnah. Morphological, Morphometric, and Distribution Pattern Characteristics of Optimal Harvest Phase Sago in Forest Area Based on Drone Imagery. Indonesian Journal of Social and Environmental Issues (IJSEI) 2024;5:318–34. https://doi.org/10.47540/ijsei.v5i3.1720
  39. Osozawa K. Sago palm and sago production in South Sulawesi: Essay on tropical low land development 1990. https://cir.nii.ac.jp/crid/1573387449830932736
  40. Sari DR. Agronomic Prospects for New Sago Palm Cultivation by farmers: Time to Harvest and Associated Cultivation Management. The 14th International Sago Symposium SAGO 2023 TOKYO 2023;85. https://www.sagopalmsociety.com/_files/ugd/3f58e5_398fcb790b40418ab832df78c76a3039.pdf#page=90
  41. Geng R, Jin S, Fu B, Wang B. Object-based wetland classification using multi-feature combination of ultra-high spatial resolution multispectral images. Canadian Journal of Remote Sensing 2020;46:784–802. https://doi.org/10.1080/07038992.2021.1872374
  42. Lastilla L, Belloni V, Ravanelli R, Crespi M. DSM generation from single and cross-sensor multi-view satellite images using the new agisoft metashape: The case studies of Trento and Matera (Italy). Remote Sens (Basel) 2021;13:593. https://doi.org/10.3390/rs13040593
  43. Guerra-Hernández J, Díaz-Varela RA, Ávarez-González JG, Rodríguez-González PM. Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests. For Ecosyst 2021;8:61. https://doi.org/10.1186/s40663-021-00342-8
  44. Hossain MD, Chen D. Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing 2019;150:115–34. https://doi.org/10.1016/j.isprsjprs.2019.02.009
  45. Difar HF, Abed FM. Automatic Extraction of Unmanned Aerial Vehicles (UAV)-based Cadastral Map: Case Study in AL-Shatrah District-Iraq. Iraqi Journal of Science 2022:877–96. https://doi.org/10.24996/ijs.2022.63.2.40
  46. Wenjie LIN, Yu LI, Quanhua Z. High-resolution remote sensing image segmentation using minimum spanning tree tessellation and RHMRF-FCM algorithm. 测绘学报 (英文版) 2020;3:52–63. https://doi.org/10.11947/j.JGGS.2020.0106
  47. Geng R, Jin S, Fu B, Wang B. Object-based wetland classification using multi-feature combination of ultra-high spatial resolution multispectral images. Canadian Journal of Remote Sensing 2020;46:784–802. https://doi.org/10.1080/07038992.2021.1872374
  48. Ez-zahouani B, Kharki OE, Idé SK, Zouiten M. Determination of Segmentation Parameters for Object-Based Remote Sensing Image Analysis from Conventional to Recent Approaches: A Review. International Journal of Geoinformatics 2023;19. https://doi.org/10.52939/ijg.v19i1.2497
  49. Norman M, Shahar HM, Mohamad Z, Rahim A, Mohd FA, Shafri HZM. Urban building detection using object-based image analysis (OBIA) and machine learning (ML) algorithms. IOP Conf Ser Earth Environ Sci 2021;620:012010. https://iopscience.iop.org/article/10.1088/1755-1315/620/1/012010/pdf
  50. Aposporis P. Object detection methods for improving UAV autonomy and remote sensing applications. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE 2020;845–53. 10.1109/ASONAM49781.2020.9381377
  51. Demarchi L, Kania A, Ciężkowski W, Piórkowski H, Oświecimska-Piasko Z, Chormański J. Recursive Feature Elimination and Random Forest Classification of Meadows and Dry Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion 2020;12:1842. https://doi.org/10.3390/rs12111842
  52. Guo Q, Zhang J, Guo S, Ye Z, Deng H, Hou X, et al. Urban tree classification based on object-oriented approach and random forest algorithm using unmanned aerial vehicle (UAV) multispectral imagery. Remote Sens (Basel) 2022;14:3885. https://doi.org/10.3390/rs14163885
  53. Liu Y, Hatou K, Aihara T, Kurose S, Akiyama T, Kohno Y, et al. A robust vegetation index based on different UAV RGB images to estimate SPAD values of naked barley leaves. Remote Sens (Basel) 2021;13:686. https://doi.org/10.3390/rs13040686
  54. Neupane B, Horanont T, Hung ND. Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV). PLoS One 2019;14:e0223906. https://doi.org/10.1371/JOURNAL.PONE.0223906
  55. Xiaoqin W, Miaomiao W, Shaoqiang W, Yundong W. Extraction of vegetation information from visible unmanned aerial vehicle images. Transactions of the Chinese Society of Agricultural Engineering 2015;31:152-159. http://www.tcsae.org/en/article/doi/10.3969/j.issn.1002-6819.2015.05.022
  56. Zhang T, Su J, Xu Z, Luo Y, Li J. Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Applied Sciences 2021;11:543. https://doi.org/10.3390/app11020543
  57. Ahmadi K, Kalantar B, Saeidi V, Harandi EKG, Janizadeh S, Ueda N. Comparison of machine learning methods for mapping the stand characteristics of temperate forests using multi-spectral sentinel-2 data. Remote Sens (Basel) 2020;12:3019. https://doi.org/10.3390/rs12183019
  58. Wu N, Crusiol LGT, Liu G, Wuyun D, Han G. Comparing machine learning algorithms for pixel/object-based classifications of semi-arid grassland in northern China using multisource medium resolution imageries. Remote Sens (Basel) 2023;15:750. https://doi.org/10.3390/rs15030750
  59. Erdanaev E, Kappas M, Wyss D. The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan. International Journal of Geoinformatics 2022;18. https://doi.org/10.52939/ijg.v18i2.2151
  60. Hao S, Cui Y, Wang J. Segmentation scale effect analysis in the object-oriented method of high-spatial-resolution image classification. Sensors 2021;21:7935. https://doi.org/10.3390/s21237935
  61. Akcay O, Avsar EO, Inalpulat M, Genc L, Cam A. Assessment of segmentation parameters for object-based land cover classification using color-infrared imagery. ISPRS Int J Geoinf 2018;7:424. https://doi.org/10.3390/ijgi7110424
  62. Ez-zahouani B, Teodoro A, Kharki OE, Jianhua L, Kotaridis I, Yuan X, et al. Remote sensing imagery segmentation in object-based analysis: A review of methods, optimization, and quality evaluation over the past 20 years. Remote Sens Appl 2023;32:101031. https://doi.org/https://doi.org/10.1016/j.rsase.2023.101031.
  63. Sexton T, Sankaran S, Cousins AB. Predicting photosynthetic capacity in tobacco using shortwave infrared spectral reflectance. J Exp Bot 2021;72:4373–83. https://doi.org/10.1093/jxb/erab118
  64. Wang Z, Nie C, Wang H, Ao Y, Jin X, Yu X, et al. Detection and analysis of degree of maize lodging using UAV-RGB image multi-feature factors and various classification methods. ISPRS Int J Geoinf 2021;10:309. https://doi.org/10.3390/ijgi7110424
  65. Dong J, Zhang J, Zhang S, Yu Z, Song Z, Meng T. Vegetation extraction through UAV RGB imagery and efficient feature selection. PLoS One 2025;20:e0322180-. https://doi.org/https://doi.org/10.1371/journal.pone.0322180.
  66. Lai S, Li Z, Ming D, Long J, Wei Y, Zhang J. A Multi-Level Segmentation Method for Mountainous Camellia oleifera Plantation with High Canopy Closure Using UAV Imagery. Agronomy 2025;15:2522. https://doi.org/10.3390/agronomy15112522.
  67. Pranga J, Borra-Serrano I, Quataert P, Swaef TD, Nest TV, Willekens K, et al. Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning. Front Plant Sci 2024;15:1414181. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1414181/full
  68. Jiménez-Lao R, Aguilar MA, Ladisa C, Aguilar FJ, Nemmaoui A. Multiresolution Segmentation For Extracting Plastic Greenhouses From Deimos-2 Imagery. Isprs Ann Photogramm Remote Sens Spatial Inf Sci 2022;2:251–8. https://doi.org/10.5194/isprs-annals-V-2-2022-251-2022.
  69. Reinprecht V, Kieffer DS. Application of UAV Photogrammetry and Multispectral Image Analysis for Identifying Land Use and Vegetation Cover Succession in Former Mining Areas. Remote Sensing 2025;17:405. https://doi.org/10.3390/RS17030405.
  70. Kior A, Yudina L, Zolin Y, Sukhov V, Sukhova E. RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review. Plants 2024;13. https://doi.org/10.3390/plants13091262.
  71. Deng H, Zhang W, Zheng X, Zhang H. Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image. Agriculture 2024;14:548. https://doi.org/10.3390/AGRICULTURE14040548.
  72. Chang B, Li F, Hu Y, Yin H, Feng Z, Zhao L. Application of UAV remote sensing for vegetation identification: a review and meta-analysis. Front Plant Sci 2025;16:1452053. https://doi.org/10.3389/FPLS.2025.1452053.
  73. Letsoin SMA, Purwestri RC, Rahmawan F, Herak D. Recognition of Sago Palm Trees Based on Transfer Learning Remote Sensing 2022;14:4932. https://doi.org/10.3390/RS14194932.
  74. Hidayat S, Matsuoka M, Baja S, Rampisela DA. Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery. Remote Sensing 2018;10:1319. https://doi.org/10.3390/RS10081319.
  75. Sun Z, Wang Y, Pan L, Xie Y, Zhang B, Liang R, et al. Pine wilt disease detection in high-resolution UAV images using object-oriented classification. Journal of Forestry Research 2021;33:1377–89. https://doi.org/10.1007/S11676-021-01420-X.
  76. Aldaeri ASTM, Kit CY, Ting LS, Rahman MRBA. Deep Learning for Tree Crown Detection and Delineation Using UAV and High-Resolution Imagery for Biometric Parameter Extraction: A Systematic Review Forests 2026;17:179. https://doi.org/10.3390/f17020179 https://www.mdpi.com/1999-4907/17/2/179
  77. Eskandari R, Mahdianpari M, Mohammadimanesh F, Salehi B, Brisco B, Homayouni S. Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models. Remote Sensing 2020;12:3511. https://doi.org/10.3390/RS12213511.
  78. Feng C, Zhang W, Deng H, Dong L, Zhang H, Tang L, et al. A combination of OBIA and random forest based on visible UAV remote sensing for accurately extracted information about weeds in areas with different weed densities in farmland. Remote Sens (Basel) 2023;15:4696. https://doi.org/10.3390/rs15194696
  79. Räsänen A, Virtanen T. Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes. Remote Sens Environ 2019;230:111207. https://doi.org/10.1016/J.RSE.2019.05.026.
  80. Dimara PA, Auri A. Effect of Landform on the Distribution of Metroxylon sagu Habitat in Yapen Islands, Papua Province, Indonesia. Jurnal Sylva Lestari 2023;11:79–97. https://doi.org/10.23960/JSL.V11I1.633.