Simulation and visualization of soybean growth affected by different amount of nitrogen, phosphorus and potassium

Abstract

This thesis proposed a simulation model of soybean growth which is effected by major nutrient factors, nitrogen, phosphorus and potassium. A feedforward neural network is used as a basis of the modelling. The combination of different percentage of nitrogen, phosphorus, potassium, time steps, and the collected height data of the soybean are used as inputs. The model can predict the height at designated time intervals, whereby the result can be visualized with Lindenmayer systems.