Optimization of Crop Planting Strategy Based on Genetic Algorithm

随着乡村全面振兴的不断推进，农村农业发展已成为国家发展的重要基石。优化种植结构、合理利用农田、降低生产成本、提高生产效益成为农村发展的重要举措之一。本文基于某乡村的耕地和农作物种类以及2023年的农作物亩产量、销售成本等数据，综合实际和问题背景，针对问题一，基于遗传算法的混合线性模型，利用农作物的生长规律和地块农作物的种植约束，分别对滞销浪费和超出降价50%两种情况的农作物种植策略进行数学建模和优化。由Python结果可得，在滞销浪费下，可预测在2024~2030年总利润中粮食类产物占大部分，且年平均总收益达32,159,289.9元；在超出降价50%中，蔬菜类在总利润中占主导，年平均总收益约32,159,568.3元。With the continuous advancement of comprehensive rural revitalization, the development of agriculture in rural areas has become a crucial cornerstone of national progress. Optimizing the planting structure, making rational use of farmland, reducing production costs, and enhancing production efficiency have emerged as significant measures for rural development. This paper is based on the arable land and crop types of a certain village, as well as the per-mu yield and sales costs of crops in 2023. Integrating practical considerations and the problem context, for Problem 1, a hybrid linear model based on a genetic algorithm is employed. Utilizing the growth patterns of crops and the planting constraints of the land, mathematical modeling and optimization are conducted for crop planting strategies under two scenarios: unsold waste and a 50% price reduction for overproduction. The Python results indicate that under the unsold waste scenario, grain crops are predicted to dominate the total profits from 2024 to 2030, with an average annual total revenue reaching 32,159,289.9 yuan. In the case of a 50% price reduction for overproduction, vegetable crops dominate the total profits, with an average annual total revenue of approximately 32,159,568.3 yuan.