Research on the Optimal Planting Plan Model Based on Improved Genetic Algorithm

This paper presents a study on the optimal planting plan model for agricultural crops using an improved genetic algorithm. The research commences with data preprocessing, addressing issues of missing values and outliers in the statistical data of agricultural planting for the year 2023. Building upon this analysis, an optimal planting plan model was constructed using linear programming, with decision variables representing the planting area of various crops across different land types and seasons. The objective function aimed to maximize the total profit from 2024 to 2030, subject to constraints including land type restrictions, crop rotation requirements, and planting area distribution. To solve this model, an improved genetic algorithm was employed, featuring multi-matrix chromosome encoding, crossover, and mutation strategies, along with an infeasible solution repair strategy to ensure constraint adherence. The algorithm's performance was evaluated through a series of experiments, demonstrating convergence towards an optimal solution with increasing iterations. The study concludes that the improved genetic algorithm is effective in identifying an optimal planting plan that maximizes profit while satisfying various agricultural constraints. Sensitivity analysis was also conducted to assess the impact of varying planting area constraints on profit fluctuations, indicating the model's robustness.