A Novel Virtual Modeling Approach for Mango Tree Biomass Estimation Using Allometric Equations and Functional-Structural Plant Modeling (FSPM) with GroIMP

Biomass represents a critical ecological contribution of trees, including mango (Mangifera indica L.), necessitating rigorous academic approaches for quantification and modeling. This study develops a computational model to enhance understanding of mango tree morphology and physiology using Functional-Structural Plant Modeling (FSPM) implemented through GroIMP software. Comparative analysis reveals that the FSPM-based biomass model generates higher estimates than traditional allometric equations, though both approaches exhibit similar temporal trends. Longitudinal data demonstrate trunk diameter progression from 1.60 cm in year one to 20.35 cm in year twenty, corresponding to biomass increases from 0.35 kg to 263.33 kg. Quantitative validation using Euclidean Distance, Error, and Similarity metrics reveals significant discrepancies between model predictions and empirical data, particularly during early growth stages, with average Euclidean Distance of 54.73, average Error of 34.12%, and average Similarity of 65.88%. These findings highlight both the potential and limitations of FSPM approaches for mango biomass estimation, providing a foundation for improved cultivation management practices and predictive modeling refinement.