Exploration of Crop Planting Strategies Based on Simulated Annealing Algorithm and VIF Tests

The aim of this paper is to explore the planting strategy based on simulated annealing algorithm and VIF test by analyzing the planting decision in order to achieve the efficient use of resources and sustainable development of green industry. The study first assumed the ratio of total planted crop production to expected sales in 2023, and constructed a multiobjective planning model using the planting of different types of plots from 2024 to 2030 as decision variables. The improved simulated annealing algorithm solves the model through the outer-point penalty function method and the centered log-ratio transformation to derive the expected returns under the two sales strategies and analyze the sensitivity and stability of the model. In addition, this paper establishes a stochastic planning model based on the consideration of potential risks, and applies the sampling approximate average method to solve the model to further optimize the planting strategy. The problem of multicollinearity among crops was verified through the VIF test, and ridge regression was utilized to improve the interpretability of the model. Ultimately, the results of the study showed an increase in expected returns under different cropping strategies and suggested feasible cropping improvements. This study provides a practical theoretical basis for agricultural decisionmaking, which is of great significance for improving agricultural productivity and promoting sustainable development.