高精度作物推系统与堆叠组合分类器,以优化农业生产率
Rania A Ahmed1,2, Walid El-Shafai3,4, Zeinab A Ahmed5
1Climate Change Information Center and Renewable Energy and Expert System, Agricultural Research Center (ARC), Giza, Egypt. rania_abdelmordy@el-eng.menofia.edu.eg.
Scientific reports
|December 8, 2025
概括
本研究介绍了一种先进的作物推系统,使用特征融合和组合模型来提高作物产量. 这种新的方法显著提高了准确性,并减少了过度装配,以改善农业决策.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 农作物生产率对于全球粮食安全和经济稳定至关重要.
- 产量受到气候,天气和土壤营养水平等因素的影响.
- 需要有效的作物推系统来优化农业实践.
研究的目的:
- 利用特征融合和整体机器学习开发一个增强的作物推系统.
- 提高作物产量预测模型的准确性和减少过度拟合.
- 为农民提供基于环境因素的最佳作物选择的数据驱动洞察力.
主要方法:
- 实现了一个堆叠组合模型,有18个分类器.
- 引入了三种用于特征融合和过拟合缓解的新方法.
- 在两个数据集上验证了模型,其中一个数据集有28,242条记录.
主要成果:
- 功能融合提高了准确性和精度,性能优于现有技术.
- 拟议的模型显示了减少过拟合,特别是在大型数据集上.
- 集成模型在作物分类中实现了从98.4%到99.54%的准确性.
- 一个投票集团分类器在一个小数据集上达到99.56%的准确性.
- 一个堆叠集团分类器在一个大数据集上实现了85.6%的准确性.
结论:
- 功能融合有效地提高了整体作物推系统的性能.
- 开发的模型提供了一个强大的解决方案,通过数据驱动的建议来提高作物生产率.
- 这项研究强调了先进机器学习技术在精准农业中的潜力.
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