Optimal Crop Planting Strategy for a Rural Village Considering Uncertain Factors

With the development of China's crop planting industry and rural economy, it is especially important to improve the planting efficiency. This paper presents a genetic algorithm-based decision model to maximize profits in North China's mountainous crop planting. Through data preprocessing, details about the data can be obtained from the following source: agdata.cn/dataManual/dataTable/MTE5Njkx.html. the median floating unit price is determined, and the planting returns at different times and land types are analyzed to clarify the expected sales volume of each type of crop in 2023. Aiming to maximize profits from 2024 to 2030, an optimization model with 11 constraints is developed. Constraints are simplified using four methods, and a genetic algorithm optimizes the objective function, with dynamic programming enhancing the fitness function to find the best planting strategy. The results of the study provide a scientific basis and decision support for crop cultivation in the mountainous areas of North China, and provide a reference for solving similar problems in other regions, which has important application value.

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Publication Details

Journal
Highlights in Science Engineering and Technology
Published
2025-05-23
DOI
https://doi.org/10.54097/z6vaf417
Primary Topic
Optimization and Mathematical Programming
Type
article
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article

Optimal Crop Planting Strategy for a Rural Village Considering Uncertain Factors

Jianjun Zhu, Yunze Wang, Yujie Jiao
Highlights in Science Engineering and Technology
Optimization and Mathematical Programming
article

Optimal Crop Planting Strategy for a Rural Village Considering Uncertain Factors

Jianjun Zhu, Yunze Wang, Yujie Jiao
article en

Abstract

With the development of China's crop planting industry and rural economy, it is especially important to improve the planting efficiency. This paper presents a genetic algorithm-based decision model to maximize profits in North China's mountainous crop planting. Through data preprocessing, details about the data can be obtained from the following source: agdata.cn/dataManual/dataTable/MTE5Njkx.html. the median floating unit price is determined, and the planting returns at different times and land types are analyzed to clarify the expected sales volume of each type of crop in 2023. Aiming to maximize profits from 2024 to 2030, an optimization model with 11 constraints is developed. Constraints are simplified using four methods, and a genetic algorithm optimizes the objective function, with dynamic programming enhancing the fitness function to find the best planting strategy. The results of the study provide a scientific basis and decision support for crop cultivation in the mountainous areas of North China, and provide a reference for solving similar problems in other regions, which has important application value.

Highlights in Science Engineering and TechnologyVol. 140
Openalex Percentile: Top 88%
Optimization and Mathematical Programming
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Optimal Crop Planting Strategy for a Rural Village Considering Uncertain Factors — Jianjun Zhu, Yunze Wang, et al. · Highlights in Science Engineering and Technology (2025) | TGRS Research Map | TGRS