Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method
Abstract Effective dynamic agricultural planning is crucial for optimising resource allocation and ensuring income stability, yet conventional methods often face limitations in adapting to the complex and variable conditions of mountainous regions, particularly under fluctuating climate and market p...
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Nature Portfolio
2025-08-01
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| Online Access: | https://doi.org/10.1038/s41598-025-14188-5 |
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| author | Changlong Li Zengye Su Yudan Nie Zhiyi Ye Jinyi Li Jing Wang Zicong Yang Xuxin Li Weijian Zeng Yanjian Chen |
| author_facet | Changlong Li Zengye Su Yudan Nie Zhiyi Ye Jinyi Li Jing Wang Zicong Yang Xuxin Li Weijian Zeng Yanjian Chen |
| author_sort | Changlong Li |
| collection | DOAJ |
| description | Abstract Effective dynamic agricultural planning is crucial for optimising resource allocation and ensuring income stability, yet conventional methods often face limitations in adapting to the complex and variable conditions of mountainous regions, particularly under fluctuating climate and market pressures. Therefore, this study introduces a novel multi-stage dynamic optimization framework specifically designed for crop planning in such challenging terrains. This framework is underpinned by a sophisticated model integrating advanced monitoring systems with a Hybrid Simulated Annealing Genetic Algorithm (H-SAGA), further enhanced by neural network-driven real-time predictions. The H-SAGA component optimises planting strategies by synergistically combining global exploration (SA) and local refinement (GA) capabilities, while the neural network dynamically adjusts revenue forecasts based on climatic and market data, significantly improving the model’s responsiveness and adaptability. We rigorously evaluated the applicability and effectiveness of this model through extensive simulations across 7,290 mu (1,201 acres) of diverse farmland in mountainous Northern China. The results demonstrate that the proposed H-SAGA approach consistently achieves 5–10 percentage points higher profit increment ratios than other benchmark optimization algorithms (such as GA, SA, PSO, and ACO), alongside faster convergence and notable robustness against environmental and economic variability. This research establishes an integrated “monitoring-modelling-decision” paradigm, driven by multi-source data and machine learning, offering a practical and robust tool that provides valuable guidance for enhancing resource allocation efficiency and promoting sustainable precision agriculture in complex topographical regions, thereby holding significant reference value for optimising agricultural production nationwide. |
| format | Article |
| id | doaj-art-0fae1c810d444e72ad807f35ca1966d9 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0fae1c810d444e72ad807f35ca1966d92025-08-20T03:05:17ZengNature PortfolioScientific Reports2045-23222025-08-0115112210.1038/s41598-025-14188-5Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA methodChanglong Li0Zengye Su1Yudan Nie2Zhiyi Ye3Jinyi Li4Jing Wang5Zicong Yang6Xuxin Li7Weijian Zeng8Yanjian Chen9School of Information Technology and Engineering, Guangzhou College of CommerceSchool of Information Technology and Engineering, Guangzhou College of CommerceSchool of Information Technology and Engineering, Guangzhou College of CommerceSchool of Economics, Guangzhou College of CommerceSchool of Digital Economy Industry, Guangzhou College of CommerceSchool of Modern Information Industry, Guangzhou College of CommerceSchool of Information Technology and Engineering, Guangzhou College of CommerceSchool of Information Technology and Engineering, Guangzhou College of CommerceSchool of Information Technology and Engineering, Guangzhou College of CommerceSchool of Information Technology and Engineering, Guangzhou College of CommerceAbstract Effective dynamic agricultural planning is crucial for optimising resource allocation and ensuring income stability, yet conventional methods often face limitations in adapting to the complex and variable conditions of mountainous regions, particularly under fluctuating climate and market pressures. Therefore, this study introduces a novel multi-stage dynamic optimization framework specifically designed for crop planning in such challenging terrains. This framework is underpinned by a sophisticated model integrating advanced monitoring systems with a Hybrid Simulated Annealing Genetic Algorithm (H-SAGA), further enhanced by neural network-driven real-time predictions. The H-SAGA component optimises planting strategies by synergistically combining global exploration (SA) and local refinement (GA) capabilities, while the neural network dynamically adjusts revenue forecasts based on climatic and market data, significantly improving the model’s responsiveness and adaptability. We rigorously evaluated the applicability and effectiveness of this model through extensive simulations across 7,290 mu (1,201 acres) of diverse farmland in mountainous Northern China. The results demonstrate that the proposed H-SAGA approach consistently achieves 5–10 percentage points higher profit increment ratios than other benchmark optimization algorithms (such as GA, SA, PSO, and ACO), alongside faster convergence and notable robustness against environmental and economic variability. This research establishes an integrated “monitoring-modelling-decision” paradigm, driven by multi-source data and machine learning, offering a practical and robust tool that provides valuable guidance for enhancing resource allocation efficiency and promoting sustainable precision agriculture in complex topographical regions, thereby holding significant reference value for optimising agricultural production nationwide.https://doi.org/10.1038/s41598-025-14188-5Hybrid H-SAGAAgricultural optimizationCrop-planningResource efficiencyClimate adaptationNorthern China Mountains |
| spellingShingle | Changlong Li Zengye Su Yudan Nie Zhiyi Ye Jinyi Li Jing Wang Zicong Yang Xuxin Li Weijian Zeng Yanjian Chen Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method Scientific Reports Hybrid H-SAGA Agricultural optimization Crop-planning Resource efficiency Climate adaptation Northern China Mountains |
| title | Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method |
| title_full | Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method |
| title_fullStr | Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method |
| title_full_unstemmed | Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method |
| title_short | Scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid SA-GA method |
| title_sort | scientific planning of dynamic crops in complex agricultural landscapes based on adaptive optimization hybrid sa ga method |
| topic | Hybrid H-SAGA Agricultural optimization Crop-planning Resource efficiency Climate adaptation Northern China Mountains |
| url | https://doi.org/10.1038/s41598-025-14188-5 |
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