OPTIMIZING CROP PLANTING PLANS BASED ON GENETIC ALGORITHMS

With the expansion of agricultural production scale and diversification of market demands, scientific and rational crop planting planning is of great significance for improving agricultural production efficiency. This study aims to optimize crop planting plans using genetic algorithms to solve this complex multi-dimensional decision problem. The research establishes an optimization model with profit maximization as the objective, considering multiple constraints including land type restrictions, crop rotation requirements, and crop distribution. Two sales scenarios were designed: unsalable when exceeding expected sales volume (Scenario 1) and selling at half price (Scenario 2). Through an improved genetic algorithm utilizing multi-matrix chromosome coding, the study effectively handles multi-dimensional decision variables involving plots, years, seasons, and crops. Results show that Scenario 2 yields significantly higher profits (1.4×107 yuan) compared to Scenario 1 (2.9×106 yuan). In terms of crop yield distribution, cowpea, sword bean, kidney bean, potato, and tomato rank as the top five; regarding cultivated land area distribution, dry land shows the highest utilization rate, indicating its superior economic benefits. This study provides a practical decision-support tool for agricultural production planning.