The Quantitative Features Analysis of the Nonlinear Model of Crop Production by Hybrid Soft Computing Paradigm

In this study, we provide a discretized system of continuous dynamical model for enhancing crop production in the presence insecticides and insects. Crops are assumed to grow logistically but limited by an insect population that entirely depends on agriculture. To protect crops from insects, farmers use insecticides, their overmuch is harmful human health. We external efforts proportional gap between actual carrying capacity increase field’s development potential. Levenberg–Marquardt algorithm (LMA) based artificial neural networks (NNs) investigate approximate solutions different insecticide spraying rates. “NDSolve” tool Mathematica generated data collection supervised LMA. The NN-LMA approximation’s value achieved training, validation, testing reference sets. Regression, error histograms, complexity analysis help validate technique’s robustness accuracy.