Crop Planting Strategy Based on Genetic Algorithm

我国作为农业大国，优化农作物种植策略对农业经济可持续发展具有重要意义。本研究针对产销失衡与不确定性决策场景，旨在提出有效的种植优化方法。对于产大于销的情况，通过作物分类与线性规划建模，实现种植收益最大化。同时，为应对市场波动风险，创新性地融合正态分布模型与遗传算法，构建风险响应决策框架。研究发现，所构建的动态决策模型能够显著提升种植方案的抗风险能力。本研究成果为制定差异化农业补贴政策、动态调整种植结构提供了量化决策依据，为乡村农作物种植规划提供有效的理论支持和决策参考。As a major agricultural country, optimizing crop planting strategies holds significant importance for the sustainable development of China’s agricultural economy. This study addresses production-marketing imbalance and uncertain decision-making scenarios, aiming to propose effective planting optimization methods. For situations where production exceeds sales, we achieve maximization of planting profits through crop classification and linear programming modeling. Meanwhile, to counter market fluctuation risks, we innovatively integrate normal distribution models with genetic algorithms to construct a risk-responsive decision-making framework. The research demonstrates that the constructed dynamic decision-making model can significantly enhance the risk resistance capability of planting schemes. These findings provide a quantitative decision-making basis for formulating differentiated agricultural subsidy policies and dynamically adjusting planting structures, offering effective theoretical support and decision-making references for rural crop planting planning.