A Multi-Objective Optimization Model for Agricultural Crop Planting Strategies Using Enhanced Genetic Algorithms and Big Data Analysis

The purpose of this paper is to construct an optimization model of crop planting strategy based on improved genetic algorithm. In order to improve the efficiency of agricultural planting and maximize the economic benefits, this paper combines the linear programming model and the multi-matrix improved genetic algorithm (MI-GA), the improved NSGA-II algorithm of orthogonal experimental design, and the multiple nonlinear regression model based on heuristic exploration algorithm to explore the complex relationship between crop sales price, planting cost and yield per mu. Through the comprehensive analysis of planting constraints, market fluctuations and risk assessment of different crops, a more scientific and practical optimal planting scheme was proposed. Experimental results show that the constructed model has good convergence and optimization effect under the condition of multi-parameter change, which provides decision support for agricultural production and helps to realize the sustainable development of rural economy.