Machine Learning and Polynomial – L System Algorithm for Modeling and Simulation of Glycine Max (L) Merrill Growth

Abstract

The agricultural sector really needs an application that able to estimate the effect of fertilization on plant growth patterns. The paper proposed the three dimensional (3D) simulation plant growth’s model of Glycine Max (L) Merrill/soybean plant using machine learning Multi-Layered Perceptron (MLP) method combine with Polynomial-Lindenmayer (Poly-L) system. The modeling parameters are the trunk/branches growth (L), the leaves width (W), and the number of branching (B) as the function of changes Nitrogen (N), Phosphate (P), and Potassium (K) elements in the fertilization process. The L, W, and B are modeled as the function of N, P, and K input using MLP method. Then, L, W, and B output are used as a variable to visualize plant growth into a 3D plant’s structure using the Poly-L System interpretation. The polynomial equation is used as a weighted factor according to the iteration of the L-System routine. The experimental results show that the MLP method is quite adaptable to the various changes of N, P, and K values and able to estimate the L, W, and B output. The average error of the trunk's growth prediction is 3.63%, the average error of leaf's width prediction is 3.72%, and the average error on the prediction of the branching's growth is 4.27%. The final result proved that the change of N, P, and K composition influenced the Poly-L System frames. Overall, the system has been running as expected.