Research on Agricultural Economic Crop Planting Decision-making Based on Greedy-Simulated Annealing Algorithm

Xinyao Cai

doi:10.54097/xs8ajg59

Authors

Xinyao Cai

DOI:

https://doi.org/10.54097/xs8ajg59

Keywords:

Agricultural Economic Optimization, Mixed-Integer Linear Programming, Greedy Algorithm, Simulated Annealing, Crop Cultivation Strategy.

Abstract

With the global population explosion and accelerated urbanization, food security and sustainable agricultural development are facing serious challenges, especially in China. How to make full use of limited arable land resources to meet the increasingly diversified demand for farm products has become a key issue in agricultural modernization. Under the actual situation of a mountain village in North China, this paper constructs a mixed integer linear programming model to optimize the planting scheme and improve local planting efficiency. The model combines a greedy algorithm with a simulated annealing algorithm, takes crop varieties and economic benefits into account, ensures a reasonable balance between grain crops and economic crops, and determines the planting priority of different crops based on key indicators such as planting cost, mu yield, and market price. The results show that the economic crop planting option is more advantageous under the stagnant sales scenario, with a profit scale of 2.32 million yuan; In contrast, the grain crop planting option is more economically efficient under the sales scenario with falling prices, with an economic crop profit scale of 13.57 million yuan. The study provides theoretical support and optimization references for rural planting planning with limited arable land resources and helps promote agricultural modernization and sustainable rural development.

Downloads

Download data is not yet available.

References

[1] Yang, H., Z. Zheng and C. Sun, E-Commerce Marketing Optimization of Agricultural Products Based on Deep Learning and Data Mining. Comput Intell Neurosci, 2022. 2022: 6564014.

[2] Talukdar, G., A.K. Sarma and R.K. Bhattacharjya, integrating optimization and damage estimation to increase economic benefit and ensure food security under seasonal land variability. [J] Environ Manage, 2022. 320: 115872.

[3] Duan, D., Study on Sustainable Agricultural Structure Optimization Method Based on Multiobjective Optimization Algorithm. Comput Intell Neurosci, 2022. 2022: 5850684.

[4] Fang, H., et al., A greedy approach for mutual exclusivity analysis in cancer study. Biostatistics, 2022. 23 (3): 910 - 925.

[5] Sharma, A., et al., Low peak power multiband spokes pulses for B1 (+) inhomogeneity-compensated simultaneous multislice excitation in high field MRI. Magn Reson Med, 2015. 74 (3): 747 - 55.

[6] Wang, F., et al., Recovering Power Grids Using Strategies Based on Network Metrics and Greedy Algorithms. Entropy (Basel), 2023. 25 (10).

[7] Hosseini, F.S., et al., Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. Sci Total Environ, 2020. 711: 135161.

[8] Benvenga, M., et al., Optimizing sheep growth curves using a meta-heuristic algorithm. Trop Anim Health Prod, 2024. 56 (8): 343.

[9] Liu, X., et al., Opportunity Analysis of Phosphorus Recovery from Municipal Wastewater for Cropland Based on the Simulated Vehicle Transport Distance in the Yangtze River Delta, China. Environ Sci Technol, 2024. 58 (31): 13726 - 13736.

[10] Rangarajan, H., et al., multi-objective optimization of root phenotypes for nutrient capture using evolutionary algorithms. Plant [J], 2022. 111 (1): 38 - 53.

[11] Khvatkov, P. and S. Dolgov, Using Mathematical Optimization Models to Improve the Efficiency of Duckweeds (Wolffia arrhiza and Lemna minor) Micropropagation. Methods Mol Biol, 2024. 2827: 85 - 98.

[12] Rahimi, M. and H. Ebrahimi, Data driven of underground water level using artificial intelligence hybrid algorithms. Sci Rep, 2023. 13(1): 10359.

[13] Kumar, V., et al., Can productivity and profitability be enhanced in intensively managed cereal systems while reducing the environmental footprint of production? Assessing sustainable intensification options in the breadbasket of India. Agric Ecosyst Environ, 2018. 252: 132 - 147.

[14] Naghavi, S., et al., Evaluation of water-energy-food-environment-agricultural economic growth nexus integrated approach to achieve sustainable production. Environ Sci Pollut Res Int, 2023. 30 (43): 96715 - 96725.

[15] Dodd, W., et al., Factors Associated with Seasonal Food Insecurity among Small-Scale Subsistence Farming Households in Rural Honduras. Int J Environ Res Public Health, 2020. 17 (3).

[16] Jeffries, C.D., et al., A greedy regression algorithm with coarse weights offers novel advantages. Sci Rep, 2022. 12 (1): 5440.

[17] Bouamama, S., C. Blum and P. Pinacho-Davidson, A Population-Based Iterated Greedy Algorithm for Maximizing Sensor Network Lifetime. Sensors (Basel), 2022. 22 (5).

[18] Goodarzian, F., et al., A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms. [J] Environ Manage, 2023. 328: 116892.

[19] Robinson, A.J., et al., Techno-economic optimization of a process superstructure for lignin valorization. Bioresour Technol, 2022. 364: 128004.

[20] Lian, Y., et al., Optimization of Sensors Data Transmission Paths for Pest Monitoring Based on Intelligent Algorithms. Biosensors (Basel), 2022. 12 (11).

[21] Molla, A., et al., Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach. Sci Total Environ, 2022. 802: 149728.