Research On Rational Organic Farming Practices Based on A Multifactor Optimization Model Using Genetic Algorithm

Zhiyue Wang; Yining Guo; Yunsheng Deng

doi:10.54097/68t0pg10

Authors

Zhiyue Wang
Yining Guo
Yunsheng Deng

DOI:

https://doi.org/10.54097/68t0pg10

Keywords:

Multi-objective Optimization Model, K-means Clustering Algorithm, Genetic Algorithm.

Abstract

Given the global population growth and the shrinking availability of arable land, it is crucial to adapt organic farming techniques to the specific characteristics of rural areas. This approach aims to maximize the utilization of available land and promote the economic development of rural communities. This paper introduces a multifactor optimization strategy to identify the most profitable agricultural farming practices. The model considers various factors, including seasonality, economic conditions, land suitability for planting, climate conditions, market prices, and planting costs. Additionally, it takes both the substitutability and complementarity of crop options into account. By employing a genetic algorithm (GA), the model is designed to identify the highest productivity planting schemes, addressing the complexity and multi-objective nature of agricultural planning. The results demonstrate that the proposed optimization strategy can significantly improve the sustainability and profitability of organic farming practices, enhancing the economic returns for farmers and contributing to the development of rural communities.

Downloads

Download data is not yet available.

References

[1] Liu X, Yang D. Irrigation schedule analysis and optimization under the different combination of P and ET0 using a spatially distributed crop model[J]. Agricultural Water Management, 2021, 256: 107084.

[2] Sajid S S, Hu G. Optimizing crop planting schedule considering planting window and storage capacity[J]. Frontiers in Plant Science, 2022, 13: 762446.

[3] Lambora A, Gupta K, Chopra K. Genetic algorithm-A literature review[C]//2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon). IEEE, 2019: 380-384.

[4] Sinaga K P, Yang M S. Unsupervised K-means clustering algorithm[J]. IEEE access, 2020, 8: 80716-80727.

[5] Gunantara N. A review of multi-objective optimization: Methods and its applications[J]. Cogent Engineering, 2018, 5(1): 1502242.

[6] Jooken J, Leyman P, De Causmaecker P. A new class of hard problem instances for the 0–1 knapsack problem[J]. European Journal of Operational Research, 2022, 301(3): 841-854.

[7] Chintapalli P, Tang C S. The implications of crop minimum support price in the presence of myopic and strategic farmers[J]. European Journal of Operational Research, 2022, 300(1): 336-349.

[8] Khatun N. Applications of normality test in statistical analysis[J]. Open journal of statistics, 2021, 11(01): 113.

[9] Almeida R M, Ribeiro D M F, Rodrigues R M, et al. Fitness value curves prediction in the evolutionary process of genetic algorithms[C]//Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2021: 221-222.

[10] Candemir A, Duvaleix S, Latruffe L. Agricultural cooperatives and farm sustainability–A literature review[J]. Journal of Economic Surveys, 2021, 35(4): 1118-1144.