基于SSA-BP算法的农业生产资源优化分配战略
1Department of Tourism Management, School of History and Culture, Harbin Normal University, Harbin, 150025, China. wang198020242024@163.com.
Scientific reports
|October 14, 2025
概括
本研究介绍了一种使用搜索算法 (SSA) 和逆向传播神经网络 (BP) 的混合优化模型,以改善农业资源分配和作物产量,平衡经济和生态效益.
科学领域:
- 农业科学 农业科学
- 人工智能的人工智能
- 优化理论 优化理论
背景情况:
- 不高效的农业资源分配导致产量降低和生态影响.
- 气候变化和耕地减少需要优化资源管理,以实现可持续农业.
- 将农作物产量最大化与生态效益相平衡,是现代农业面临的关键挑战.
研究的目的:
- 开发一种混合优化模型,以提高农业资源分配和作物产量.
- 解决传统方法的局限性,例如局部最佳和缓慢的融合.
- 为智能农业管理提供工具,支持实时决策和气候适应.
主要方法:
- 一个混合模型,结合了Sparrow搜索算法 (SSA) 进行全球探索和反向传播神经网络 (BP) 进行非线性拟合.
- SSA模拟雀鸟的食和警报行为,以优化资源分配.
- 集成到SSA中的差异化进化策略,以提高模型的稳定性.
主要成果:
- 混合型号在8次代中实现了3的平均健身.
- 收益率预测的准确性超过98.5%,有2个隐藏层节点.
- 超过1.15的资源成本-产出比表明了成本效益.
结论:
- 拟议的SSA-BP混合模型有效地优化了农业资源分配,以提高作物产量和生态效益.
- 该模型为智能农业平台提供实时决策能力,帮助资源规划和气候适应.
- 通过实时调整水和肥料比率来提高资源利用率,有助于实现双重经济和生态收益.
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