Fitting Logistic Growth Model for Soybean Height Using Nonlinear Optimization with Levenberg-Marquardt
DOI:
https://doi.org/10.24191/jcrinn.v11i2.525Keywords:
Sigmoid Model, Logistic Growth Model, Nonlinear Least Squares, Levenberg Marquardt, Parameter EstimationAbstract
This study focuses on modelling soybean growth using the Logistic growth model, a simple sigmoid model with three easily interpreted parameters. The Logistic growth model is also classified as an intrinsically nonlinear model. Parameter estimation was performed using the Nonlinear Least Square (NLS) method with an iterative algorithm, specifically the Levenberg Marquardt algorithm. The results indicated that the maximum height was 21.6543 cm, the intrinsic growth rate was 0.61368, and the parameter controlling the slope of the curve was 10.5104. All parameters in the Logistic growth model were significant to the model based on the t-test results and the model had an adjusted R-squared value of 0.957113. This value indicates that the model can explain 95.71% of the growth patterns observed in the data through the parameters that have been considered.
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