Optimization of Crop Planting Strategies Using Linear Programming and Genetic Algorithm Under Complex Land Conditions

Abstract

To address the challenge of optimizing crop planting strategies under complex land conditions, this study proposes a model that combines linear programming and genetic algorithm to maximize planting profitability. While traditional linear programming excels at solving resource allocation problems, it faces limitations in handling large-scale nonlinear and dynamic constraints. To overcome these challenges, genetic algorithms are employed to further optimize planting schemes, leveraging their evolutionary search capabilities. The experimental process includes constructing a linear programming model to define the objective function and constraints, followed by solving the model using a genetic algorithm to identify optimal solutions. Results show that the proposed method significantly improves planting profitability, enhances resource utilization efficiency, and ensures crop diversity. Sensitivity analysis indicates that the model is robust and adaptable to market fluctuations, such as changes in yield-to-sale ratios and costs. This study provides an effective tool for agricultural resource optimization and offers a foundation for further research in dynamic planting systems and sustainable agricultural management.