Design and Optimization of Sugarcane Spiral Fertilizer Applicator Based on Response Surface Methodology and Artificial Neural Networks

Abstract

To address the challenges of poor fluidity and low uniformity in conventional sugarcane fertilizer applicators, a novel dual-directional spiral fertilizer applicator has been developed. The working principle of the applicator is explained, and, after analyzing the agronomic requirements for sugarcane, the parameter range for key components of the applicator is determined. The spiral blade’s diameter, pitch, and rotational velocity are chosen as the experimental factors, with the average fertilizer discharge uniformity as the evaluation criterion. Virtual simulation experiments are conducted using the discrete element method and a quadratic regression orthogonal rotating combined design. Regression models for the evaluation criterion and various experimental factors are obtained. Additionally, a dataset created from these experiments was then used to construct an artificial neural network (ANN) prediction model. Response surface methodology (RSM) and the ANN were both used to analyze and predict the outcomes. The results indicate that the artificial neural network outperforms response surface methodology in terms of better fitting capability and higher prediction accuracy. The determination coefficient, mean squared error, and root mean square error are 0.99629, 0.99163, 0.07763, 0.17498, 0.27862, and 0.41831, respectively. When comparing the two models, the optimal parameter combination is determined to be a diameter of 90.1669 mm, a pitch of 59.7407 mm, and a rotational speed of 53.8944 r/min, resulting in an average fertilizer discharge uniformity of 92.0670%. An experiment with these parameters confirmed the simulated findings, revealing a maximum discrepancy of 2.4%. This study offers valuable insights into optimizing spiral fertilizer applicators.