A Fuzzy-Expert enhanced NSGA-II approach for sustainable agricultural systems.

Zhonglin Huang, Yongjun Pu, Qianrong Zhang, Yongheng Wang, Jiechao Yang, Yueqinyun Gu

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Zhonglin HuangSchool of Network and Communication Engineering, Chengdu Technological University, Chengdu, 611730, China.

Yongjun PuIntelligent Terminal Industry College, Chengdu Technological University, Chengdu, 611730, China.

Qianrong ZhangCollege of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu, 611730, China. zqrong1@cdtu.edu.cn.

Yongheng WangSchool of Network and Communication Engineering, Chengdu Technological University, Chengdu, 611730, China.

Jiechao YangSchool of Network and Communication Engineering, Chengdu Technological University, Chengdu, 611730, China.

Yueqinyun GuSchool of Network and Communication Engineering, Chengdu Technological University, Chengdu, 611730, China.

Funding

Chunhui Project Foundation of the Education Department of China 202201245the quality of Sichuan birth Canal medicinal materials based on digital twins 2024YFFK0191

6 · The paper itself

Abstract

This study proposes a Fuzzy-Expert-NSGA-II algorithm, an enhanced NSGA-II approach incorporating fuzzy expert systems, for multi-objective optimization of agricultural planting strategies. The framework integrates expert rules, fuzzy mathematics, and evolutionary computation to establish a comprehensive optimization system considering economic benefits, ecological sustainability, and management efficiency. Using Chehe Village in Shanxi Province, China as a case study, we developed a multi-period crop planning model encompassing 41 crop types and 54 cultivated plots, incorporating constraints related to land types, crop rotation rules, and market uncertainties. The algorithm innovatively introduces a Hybrid Adaptive Local Search (HALS) mechanism and an Expert Rule-based Repair (ERB) module, significantly improving solution quality and feasibility. Experimental results demonstrate superior performance compared to standard NSGA-II, MOPSO, and MOEA/D, achieving better hypervolume indicator (HV = 0.892) and constraint satisfaction rate (1.2%). Simulation projections for 2024-2030 indicate that the optimized solution could increase average profits by 23% while maintaining high biodiversity levels (Simpson index 0.72-0.83). This research provides a data-driven decision support tool for agricultural land use planning, effectively balancing economic and ecological objectives.

Identifiers

PMID41028804

DOI10.1038/s41598-025-14488-w

PMCPMC12484540

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.