一种基于延迟发光的小麦老化新型检测方法.
Gong Yue-Hong1,2, Liu Yu-Kun3, Gong Zhi-le4
1School of Software, Pingdingshan University, Pingdingshan, 467000, China.
Scientific reports
|January 11, 2024
概括
使用延迟发光 (DL) 和沃尔什编码双向长短期记忆 (Bi-LSTM) 网络的新型非破坏性方法可以准确地检测小麦老化. 这种绿色方法达到94%的准确性,为传统方法提供了更快,更节省样本的替代方案.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 评估小麦老化对于储存质量和加工至关重要.
- 传统的化学方法是缓慢而破坏性的.
- 现有的物理方法在实践中应用有限.
研究的目的:
- 开发一种新的,非破坏性的检测小麦老化的方法.
- 为了利用延迟发光 (DL) 信号来评估小麦的质量.
- 提高小麦老化检测的准确性和效率.
主要方法:
- 配合延迟发光 (DL) 信号与超标函数来提取四个衰变趋势特征.
- 提取额外的六个特征以形成一个全面的输入向量.
- 提出一个沃尔什编码的双向长短期内存 (Walsh-Bi-LSTM) 网络,以提高错误的纠正和检测.
主要成果:
- 拟议的Walsh-Bi-LSTM模型在测试数据集上实现了94.00%的准确性.
- 该模型有效地利用DL信号特征来检测衰老.
- 与传统技术相比,该方法显示出更高的性能.
结论:
- 开发的基于延迟发光的Walsh-Bi-LSTM模型为及时检测小麦老化提供了一种绿色和非破坏性的解决方案.
- 这种方法显著改善了传统化学和物理检测方法的局限性.
- 该模型显示高准确性和实际适用性,用于评估储存小麦的质量.
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