Analysis of Crop Planting Strategies Using Improved Simulated Annealing Optimization Algorithm
Year: 2025
Type: article
Abstract: Given the actual needs of farmers and the need to protect the environment, selecting suitable crop optimization planting strategies is of great significance. Based on the analysis of the impact of different types of land parcels on crop yield, crop rotation demand, and market demand changes, this article optimizes the allocation of arable land resources through an improved simulated annealing algorithm. Determine the indicators based on the given data and use the Stackelberg model to calculate the selling price, establish a single objective optimization model, and solve for the optimal planting results through an improved simulated annealing algorithm. Firstly, visualize the data to understand the features and determine the indicators, and use the Stackelberg model to calculate the selling price. Secondly, construct a single objective optimization model based on indicators and constraints. Finally, in order to simplify the calculation and improve the accuracy of the optimal solution, the plot is divided into four parts, and a preliminary solution is obtained through simulated annealing algorithm. Then, an adaptive threshold is introduced to improve the optimal planting strategy. This indicates that the improved simulated annealing algorithm can obtain the optimal solution for multiple crop planting strategies under different unsold models, demonstrating that the improved simulated annealing algorithm has made good progress in solving related crop planting strategies.
DOI: 10.61784/ejst3088
Authors: Bin Cheng, Xiang Gao, JiaWei Zhao, KunLin Yu
Institution: Wuhan University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4411102306