Predictive Planting Strategy Optimization Model for Crops Based on Linear Programming

Ronghao Zhou

doi:10.54097/yjh81450

Authors

Ronghao Zhou

DOI:

https://doi.org/10.54097/yjh81450

Keywords:

Linear Programming, Normal Distribution, RANDN Functions.

Abstract

Cropping strategies are important for crop planning in rural land. In rural areas, it is necessary to make full use of limited cultivated land resources, select suitable crops, make field management more convenient, reduce the planting risks that may be caused by various uncertain factors, maintain profits, and optimize planting strategies plays a vital role. In this paper, the method of linear programming is used to study the specific background of a simulated low temperature region in the North China Plain, and the optimal planting strategy model and the maximum return expectation optimization model are constructed with the goal of profit maximization. According to the unsalable market and the fluctuation of crop planting cost, the planting strategy with the goal of profit maximization is obtained by introducing RANDN random variables and RANDN functions, combined with the greedy algorithm to continuously optimize and approximate the global optimal solution with the local optimal solution.

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References

[1] Dobor L, Barcza Z, Hlásny T, et al. Crop planting date matters: Estimation methods and effect on future yields [J]. Agricultural and forest meteorology, 2016, 223: 103 - 115.

[2] Wu B, Li Q. Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes [J]. International Journal of Applied Earth Observation and Geoinformation, 2012, 16: 101 - 112.

[3] Sandhu N, Yadav S, Kumar Singh V, et al. Effective crop management and modern breeding strategies to ensure higher crop productivity under direct seeded rice cultivation system: A review [J]. Agronomy, 2021, 11 (7): 1264.

[4] Hajimirzajan A, Vahdat M, Sadegheih A, et al. An integrated strategic framework for large-scale crop planning: sustainable climate-smart crop planning and agri-food supply chain management[J]. Sustainable Production and Consumption, 2021, 26: 709 - 732.

[5] Amirahmadi E, Ghorbani M, Moudrý J, et al. Environmental assessment of dryland and irrigated winter wheat cultivation under compost fertilization strategies [J]. Plants, 2024, 13 (4): 509.

[6] Iswardani W. Hydroponic Vegetable Production Planning at Plantation Parung Farm Bogor [J]. AKADEMIK: Jurnal Mahasiswa Sain & Teknologi, 2021, 1(1): 8 - 15.

[7] Yan Q, Zhang M, Lin H, et al. Two-stage adjustable robust optimal dispatching model for multi-energy virtual pothis paperr plant considering multiple uncertainties and carbon trading[J]. Journal of cleaner production, 2022, 336: 130400.

[8] Zhao Z Y, Zhou M C, Liu S X. Iterated greedy algorithms for flow-shop scheduling problems: A tutorial [J]. IEEE Transactions on Automation Science and Engineering, 2021, 19 (3): 1941 - 1959.

[9] Xiang D, Lin H, Ouyang J, et al. Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot [J]. Scientific Reports, 2022, 12 (1): 13273.

[10] Tao X, Pan Q, Gao L. An iterated greedy algorithm with reinforcement learning for distributed hybrid flowshop problems with job merging [J]. IEEE Transactions on Evolutionary Computation, 2024.

[11] Hu J, Szymczak S. A review on longitudinal data analysis with random forest [J]. Briefings in Bioinformatics, 2023, 24 (2): bbad002.

[12] Peng H, Pappas N, Yogatama D, et al. Random feature attention [J]. arxiv preprint arxiv: 2103.02143, 2021.