在传感器植物疾病数据集中的特征提取使用了独立于类变量的改革成员函数
Ayushi Gupta1, Anuradha Chug2, Amit Prakash Singh3
1University School of Information, Communication & Technology, GGSIPU, Delhi, India. ayushi.20616490021@ipu.ac.in.
Scientific reports
|January 30, 2026
概括
本研究引入了一种新的基于会员函数的特征提取 (MFFE) 技术,用于增强小型传感器数据集,而不需要类变量. TMF-ORBFNN模型实现了卓越的准确性,帮助农民在早期发现作物疾病并减少农药的使用.
科学领域:
- 农业技术 农业技术
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 基于传感器的数据集通常由于部署成本和复杂性而受到限制.
- 对于机器学习模型培训来说,小型数据集,特别是在农业领域,构成了挑战.
- 现有的特征提取方法可能依赖于类变量,从而限制了它们的适用性.
研究的目的:
- 开发基于会员功能的特征提取 (MFFE) 技术,以增强基于传感器的小型植物数据集.
- 创建一个独立于类变量的特征提取方法.
- 通过使用增强数据集,提高植物疾病分类的准确性.
主要方法:
- 利用了两个基于实时传感器的番茄病数据集 (TomEBD, TPMD).
- 应用KMeans-SMOTE来解决数据集不平衡的问题.
- 为特征提取实施了改革的三角和高斯成员函数,确保参数仅从训练数据计算出来.
- 使用优化内核极端学习机器 (OKELM) 和优化辐射基函数神经网络 (ORBFNN) 的分类增强数据集,与Optuna框架调整.
- 在八个比较非植物数据集上验证了该技术.
主要成果:
- 三角成员函数-ORBFNN (TMF-ORBFNN) 模型在植物疾病和基准数据集上实现了最高的准确性.
- 统计分析 (弗里德曼和邦费罗尼-恩试验) 证实TMF-ORBFNN的业绩明显提高.
- 分析了MFFE方法的时间复杂性.
结论:
- 该MFFE技术有效地从小型基于传感器的数据集中提取特征,而不依赖类变量.
- TMF-ORBFNN模型显示了提高植物疾病分类准确性的巨大潜力.
- 这种方法可以为农民提供及时的作物疾病管理策略,从而减少农药的使用.
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