The combination of single-objective nonlinear uncertain optimization and particle swarm algorithm for optimization crop selection

Abstract

In actual agricultural sowing, due to the uncertainty of various concurrent risk types, such as the expected sales volume, yield per mu, planting cost, and selling price of various crops, a single-objective nonlinear uncertainty optimization model is established after comprehensively considering the variation of relevant indicators of various agricultural products within a certain range, to make reasonable decisions for crop planning, rotation, and dense planting. To demonstrate the applicability and feasibility of the method proposed by the model, we use actual cases from the North China region to predict the formulation strategies for agricultural cultivation in the next seven years. The calculation quickly finds the optimal solution within a smaller number of iterations and meets the different requirements under different terrain conditions such as flat and dry land, terraced fields, and hillsides. The practical verification of the model is combined with the application of the particle swarm algorithm, fully demonstrating its potential in agricultural decision-making and helping farmers make scientific planting choices in complex and changeable environments.