Crop Planting Strategy based on Greedy Algorithm and Monte Carlo Simulation

Crop cultivation is a complex process influenced by a multitude of environmental and market factors. To navigate these complexities and enhance profitability, this paper presents an innovative crop planting strategy. The strategy employs a combination of a greedy algorithm and Monte Carlo simulation techniques to optimize the allocation of crop cultivation. By analyzing historical planting and sales data, the proposed method aims to predict and maximize overall profitability. This approach not only considers immediate conditions but also incorporates a probabilistic assessment of various outcomes, providing a robust framework for decision-making in agricultural planning.