Optimization of crop planting strategies: an integrated scheme of linear programming and intelligent optimization algorithms

Abstract

To address the challenge of developing optimal planting strategies for multiple crops under the constraints of diverse greenhouse conditions and various types of cultivated land, a crop planting strategy based on intelligent optimization algorithms is proposed. First, assuming stable crop production and sales across years, a linear programming model is formulated to account for diversified planting risks. Uncertainty factors are incorporated into the decision-making process through interpolation techniques, with the dual objectives of maximizing net income and minimizing overproduction losses. Optimization and solution of the model are achieved using a combination of a greedy algorithm and a genetic algorithm, enhanced by the Pearson correlation coefficient. Sensitivity analysis is conducted to effectively evaluate the robustness and adaptability of the proposed planting strategy under different scenarios. Furthermore, the scope of the study is extended to scenarios involving year-on-year increases in crop sales, with additional considerations given to crop uncertainty and planting risks.