混合深度学习优化用于智能农业:子喉优化和极地搜索应用于水质预测
Amal H Alharbi1, Faris H Rizk2, Khaled Sh Gaber3
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
PloS one
|July 21, 2025
概括
本研究介绍了使用Dipper Throated Optimization (DTO) 和Polar Rose Search (PRS) 进行预测性水质评估的混合优化框架. 这种新的方法增强了深度学习模型,为可持续的智能灌实现了99.46%的准确性.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 精确的水资源管理对于可持续农业至关重要,特别是对于像土豆这样的高价值作物.
- 准确评估水质对于优化灌和确保作物产量至关重要.
- 现有的方法可能难以应对高维度农业数据集的复杂性.
研究的目的:
- 为预测性水质评估开发一种新的混合元启发框架.
- 加强深度学习模型,以改善智能灌系统的决策.
- 整合特征选择和元启发性优化,以实现高效的数据处理.
主要方法:
- 这是一个混合的元启发框架,它结合了Dipper Throated Optimization (DTO) 和Polar Rose Search (PRS).
- 将二进制特征选择和元启发优化集成到一个统一的过程中.
- 将混合策略应用于辐射基函数网络 (RBFN) 模型.
主要成果:
- 优化的RBFN模型实现了99.46%的分类准确度.
- 通过ANOVA和Wilcoxon测试验证实了显著的性能改善.
- 该框架在与经典和未经优化深度学习模型相比,表现优越.
结论:
- 拟议的框架提供了准确,可解释和计算效率高的水质预测.
- 这种方法支持在水资源有限的农业环境中智能灌决策.
- 该研究有助于可持续的作物生产和有效的资源保护.
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