Prediction Model of Dry Fertilizer Crushing Force Based on P-DE-SVM.

Abstract

The accurate prediction of fertilizer crushing force could reduce the crushing rate in the process of transportation and utilization and ensure the efficient utilization of the fertilizer so as to realize the sustainable and clean production of crops. To achieve this goal, a fertilizer crushing force prediction model based on the shape characteristics was proposed in this paper using the Pearson correlation coefficient, differential evolution algorithm, and the support vector machine (P-DE-SVM). First, the shape characteristics and crushing force of fertilizers were measured by an independently developed agricultural material shape analyzer and digital pressure gauge, and the shape characteristics related to the fertilizer crushing force were proposed based on the Pearson correlation coefficient. Second, a fertilizer crushing force prediction model based on a support vector machine was constructed, in which the optimal kernel function was the radial basis function. Finally, a differential evolution algorithm was proposed to optimize the internal parameters of the fertilizer-crushing force prediction model, and at the same time, a fertilizer granularity inspection range was calculated. The experimental results showed that the maximum error rate of the fertilizer crushing force prediction model was between −10.4 and 10.9%, and the fertilizer granularity inspection range was reasonable. The proposed prediction model in this paper could lay a solid foundation for fertilizer production and quality inspection, which would help reduce fertilizer crushing and improve fertilizer utilization to realize the sustainable and clean production of crops.