ORBMO-RF:一种基于多式融合和改进的红嘴蓝优化算法的人参种子的非破坏性分类方法
Mingxuan Xue1,2, Yanan Zhu2, Bin Liu2
1School of Information Technology, Jilin Agricultural University, Changchun, China.
Frontiers in plant science
|January 30, 2026
概括
这项研究引入了一种智能方法,用于使用融合图像和光谱数据对人参种子进行分类. 这种先进的模型显著提高了分类准确性,有助于人参的育种和质量控制.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 人参种子的精确分类对于生产,质量,育种和工业现代化至关重要.
- 目前针对人参种子的自动精密分类技术发展不足,面临技术瓶.
研究的目的:
- 开发一种创新的智能分类方法,使用多式联络数据融合来对人参种子进行分类.
- 为提高工业应用中人参种子分类的准确性和效率.
主要方法:
- 一种多式数据融合方法,将图像衍生的形态特征和超频谱数据结合起来.
- 用于形态特征选择的递归特征消除 (RFE) 和用于光谱带提取的竞争性适应性重权取样 (CARS).
- 一个随机森林 (RF) 分类模型,通过增强的红蓝优化 (RBMO) 算法优化,并采用混合机制来改善融合.
主要成果:
- 拟议的多式联通融合模型显示了与基线射频模型相比的显著改进.
- 在测试数据集上,准确度提高了4.69%,精度提高了4.79%,回忆率提高了4.69%,F1分数提高了4.74%.
结论:
- 开发的系统为大规模的人参种子分类提供了坚实的理论和技术基础.
- 这种智能决策范式可用于中国传统医学的非破坏性测试.
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