An Object Driven Decision Model for Quantifying the Virtual Merkus Pine Tree's Environment Contribution
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A tree planted in the wild contributes significantly to nature and its surroundings. Key benefits include biomass production and the strengthening of soil contours. Biomass itself is a tangible output of living organisms, offering both renewable fuel potential and notable economic value. Additionally, the presence of a tree has a considerable effect on soil shear strength, which plays a crucial role in supporting reforestation efforts in deforested areas. The research aims to construct a computational decision model of a virtual Merkus Pine tree to estimate biomass production and evaluate its impact on soil reinforcement as part of the tree's environmental contributions. The model was constructed via two types of methods: an object-oriented approach for technical design and functional-structural plant modeling (FSPM) as a core method to construct a 3D virtual pine tree model. The model is a novel computational decision model operated to visually simulate the growth and development of Merkus Pine, estimate biomass yield, and calculate annual soil shear strength due to the tree’s presence. Simulation results indicate that a single Merkus Pine tree can produce up to 242.27 kg of biomass and enhance soil shear strength by approximately 0.88 N by the end of 15 years.
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[1] Ashfaq, M. M., Bilgic Tüzemen, G., & Noor, A. (2024). Exploiting agricultural biomass via thermochemical processes for sustainable hydrogen and bioenergy: A critical review. International Journal of Hydrogen Energy, 84, 1068–1084. doi:10.1016/j.ijhydene.2024.08.295.
[2] Poornima, S., Manikandan, S., Prakash, R., Deena, S. R., Subbaiya, R., Karmegam, N., Kim, W., & Govarthanan, M. (2024). Biofuel and biochemical production through biomass transformation using advanced thermochemical and biochemical processes – A review. Fuel, 372. doi:10.1016/j.fuel.2024.132204.
[3] Watkins, D., Nuruddin, M., Hosur, M., Tcherbi-Narteh, A., & Jeelani, S. (2015). Extraction and characterization of lignin from different biomass resources. Journal of Materials Research and Technology, 4(1), 26–32. doi:10.1016/j.jmrt.2014.10.009.
[4] Halder, P., Kundu, S., Patel, S., Setiawan, A., Atkin, R., Parthasarthy, R., Paz-Ferreiro, J., Surapaneni, A., & Shah, K. (2019). Progress on the pre-treatment of lignocellulosic biomass employing ionic liquids. Renewable and Sustainable Energy Reviews, 105, 268–292. doi:10.1016/j.rser.2019.01.052.
[5] Song, G., Huang, D., Li, A., Li, R., Hu, S., Xu, K., Ren, Q., Han, H., Wang, Y., Su, S., & Xiang, J. (2024). Quick measurement method of three components in lignocellulosic biomass based on kinetic mechanism analysis of PT-TGA. Fuel, 367. doi:10.1016/j.fuel.2024.131521.
[6] Wang, X., Liu, Q., Yang, L., Yang, Q., Li, Y., Yang, Y., Zhou, C., Wang, X., Wang, Y., Gao, G., Liu, W., & Cheng, P. (2024). Perceptions of biomass energy sustainability in policy scenarios of China. Heliyon, 10(17), e37180. doi:10.1016/j.heliyon.2024.e37180.
[7] Gu, S., Sun, S., Wang, X., Wang, S., Yang, M., Li, J., Maimaiti, P., van der Werf, W., Evers, J. B., & Zhang, L. (2024). Optimizing radiation capture in machine-harvested cotton: A functional-structural plant modelling approach to chemical vs. manual topping strategies. Field Crops Research, 317. doi:10.1016/j.fcr.2024.109553.
[8] Clarke, S. S. B., Benzecry, A., Bokros, N., DeBolt, S., Robertson, D. J., & Stubbs, C. J. (2024). A custom pipeline for building computational models of plant tissue. European Journal of Agronomy, 161. doi:10.1016/j.eja.2024.127356.
[9] Xin, B., Smoleňová, K., Bartholomeus, H., & Kootstra, G. (2024). An automatic 3D tomato plant stemwork phenotyping pipeline at internode level based on tree quantitative structural modelling algorithm. Computers and Electronics in Agriculture, 227(2), 109607. doi:10.1016/j.compag.2024.109607.
[10] Utama, D. N., & Gunawan, I. (2023). An Object Driven Model of Above-Land Merkus Pine Tree for Quantifying the Commercial Contribution with Functional-Structural Plant Modeling. IEEE Access, 11, 138675–138686. doi:10.1109/ACCESS.2023.3338719.
[11] Sun, K., Yu, J., Zhao, J., Liang, L., Wang, Y., & Yu, Y. (2023). A DEM-based general modeling method and experimental verification for wheat plants in the mature period. Computers and Electronics in Agriculture, 214. doi:10.1016/j.compag.2023.108283.
[12] Yu, S., & Qin, H. (2023). Modeling the effects of plant uptake dynamics on nitrogen removal of a bioretention system. Water Research, 247. doi:10.1016/j.watres.2023.120763.
[13] Urso, L., Petermann, E., Gnädinger, F., & Hartmann, P. (2023). Use of random forest algorithm for predictive modelling of transfer factor soil-plant for radiocaesium: A feasibility study. Journal of Environmental Radioactivity, 270. doi:10.1016/j.jenvrad.2023.107309.
[14] Aishwarya, M. P., & Reddy, P. (2023). Ensemble of CNN models for classification of groundnut plant leaf disease detection. Smart Agricultural Technology, 6. doi:10.1016/j.atech.2023.100362.
[15] Fidan, B., Bodur, F. G., Öztep, G., Güngören-Madenoğlu, T., Kabay, N., & Baba, A. (2025). Comparison of conventional and machine learning models for kinetic modelling of biomethane production from pretreated tomato plant residues. Industrial Crops and Products, 223. doi:10.1016/j.indcrop.2024.120235.
[16] Behnia, M., Ghahderijani, M., Kaab, A., & Behnia, M. (2025). Evaluation of sustainable energy use in sugarcane production: A holistic model from planting to harvest and life cycle assessment. Environmental and Sustainability Indicators, 26. doi:10.1016/j.indic.2025.100617.
[17] Hu, M., Tang, H., Yu, Q., & Wu, W. (2025). A new approach for spatial optimization of crop planting structure to balance economic and environmental benefits. Sustainable Production and Consumption, 53, 109–124. doi:10.1016/j.spc.2024.12.003.
[18] Torquato, P. R., Szota, C., Hahs, A. K., Arndt, S. K., & Livesley, S. J. (2025). Insufficient space: Prioritizing large tree species and planting designs still fail to meet urban forest canopy targets. Landscape and Urban Planning, 256. doi:10.1016/j.landurbplan.2024.105287.
[19] Kang, G., & Kim, J. J. (2025). How to plant trees on an elevated road to improve thermal comfort in a street canyon. Sustainable Cities and Society, 121. doi:10.1016/j.scs.2025.106207.
[20] Utama, D. N. (2021). Fuzzy logic for decision support models, equipped with case study applications. Penerbit Garudhawaca, Yogyakarta, Indonesia.
[21] Halpin, T., & Morgan, T. (2024). Data Modeling in UML. Information Modeling and Relational Databases, 347–399. doi:10.1016/b978-0-443-23790-4.00001-3.
[22] Mitsanis, C., Hurst, W., & Tekinerdogan, B. (2024). A 3D functional plant modelling framework for agricultural digital twins. Computers and Electronics in Agriculture, 218. doi:10.1016/j.compag.2024.108733.
[23] Hemmerling, R., Evers, J. B., Smoleňová, K., Buck-Sorlin, G., & Kurth, W. (2013). Extension of the GroIMP modelling platform to allow easy specification of differential equations describing biological processes within plant models. Computers and Electronics in Agriculture, 92, 1–8. doi:10.1016/j.compag.2012.12.007.
[24] Doostali, S., Babamir, S. M., & Javani, M. (2023). Using a process algebra interface for verification and validation of UML statecharts. Computer Standards and Interfaces, 86. doi:10.1016/j.csi.2023.103739.
[25] Siregar, C. A. (2007). Biomass estimation in pine plantation forests (Pinus Merkusii Jungh et de Vriese) and soil carbon conservation in Ciaten, West Java. Journal of Forest Research and Nature Conservation, 4(3), 251-266. doi:10.20886/jphka.2007.4.3.251-266.
[26] Sanquetta, C. R., Corte, A. P., & da Silva, F. (2011). Biomass expansion factor and root-to-shoot ratio for Pinus in Brazil. Carbon Balance and Management, 6(1), 6. doi:10.1186/1750-0680-6-6.
[27] Docker, B. B., & Hubble, T. C. T. (2008). Quantifying root-reinforcement of river bank soils by four Australian tree species. Geomorphology, 100(3–4), 401–418. doi:10.1016/j.geomorph.2008.01.009.
[28] Zhang, Y., Henke, M., Buck-Sorlin, G. H., Li, Y., Xu, H., Liu, X., & Li, T. (2021). Estimating canopy leaf physiology of tomato plants grown in a solar greenhouse: Evidence from simulations of light and thermal microclimate using a Functional-Structural Plant Model. Agricultural and Forest Meteorology, 307. doi:10.1016/j.agrformet.2021.108494.
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