为了提高精准农业作物预测系统的性能,使用特征相关方格式基于最近邻居分类器的特征相关性
Khushal Kindra1, N G Bhuvaneswari Amma2, N G Nageswari Amma3
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.
Scientific reports
|February 12, 2026
概括
这项研究引入了一种新的基于特征相关方形的最近邻居 (FCSNN) 方法,用于准确的作物预测. 通过考虑特征相关性,FCSNN方法提高了作物产量利能力,优于现有系统.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 精准农业通过高效农业提高作物利能力.
- 准确的作物预测对于印度的农业决策至关重要.
- 现有的系统经常忽视特征相关性,影响预测准确性.
研究的目的:
- 开发具有高精度的智能作物预测系统.
- 解决现有模型中未考虑的特征相关性缺口.
- 帮助农民选择最佳的作物进行种植.
主要方法:
- 提出了一种基于特征相关方格的新近邻近 (FCSNN) 方法.
- FCSNN方法提取了作物特征之间的相关性.
- 该系统使用最近邻近算法来预测作物类型.
主要成果:
- 拟议的FCSNN方法是在基准农业数据集上进行培训和测试的.
- 与现有的作物预测系统相比,FCSNN方法显示出更高的性能.
- 考虑特征相关性显著改善了预测准确性.
结论:
- FCSNN方法提供了一种更准确的作物预测方法.
- 整合特征相关性分析对于增强农业预测模型至关重要.
- 这个系统可以显著帮助印度的农民和政策制定者.
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