Design of Crop Planting Strategy Based on Genetic Algorithm

With the intensification and intelligent development of agricultural production, optimizing planting strategies to obtain maximum profits has become increasingly important. This study uses Matlab to process data, constructs a linear programming model, and applies genetic algorithms and Monte Carlo simulations to explore strategies for optimizing crop and land allocation to maximize planting income over the next seven years under crop rotation and cost constraints. The study also considered crop market volatility, introduced alternative and complementary factors, and evaluated the impact of these factors on planting strategies and total returns through multiple regression analysis and stochastic dynamic programming models, aiming to provide comprehensive planting strategies for adapting to market and climate change.