Research on Crop Planting Strategy Problem Based on Optimization Greedy Algorithm - Li, Zhetong; Yuan, Peiqi; Zhao, Sanglin; Deng, Hao; Yuan, Bingkun (Transactions on Environment, Energy and Earth Sciences, 2024)
This article proposes a planting optimization strategy that comprehensively considers crop profits, planting costs, yield per mu, and mutual constraint effects based on crop economic and geographical data from rural areas in North China from 2024 to 2030. By combining greedy algo rithm, K-means clustering analysis, and Pearson correlation analysis, we ensure that crop selection is both efficient and sustainable, while adhering to crop rotation principles to avoid repeated cropping. The results show that the optimized planting plan has a total profit of 86382 538.00 yuan between 2024 and 2030, an increase of 211.22% compared to the original strategy profit. The study not only verified the scientific and feasibility of the model, but also provided specific planting strategy suggestions for agricultural production, aiming to improve agricultural production efficiency and promote the sustainable use of land resources.