基于电容度方法和Logistic-SSA-ELM建模的狐尾小米水分含量的非破坏性和快速检测技术的研究
Zhichao Qiu1, Gangao Li1, Zongbao Huang2
1College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China.
Frontiers in plant science
|June 14, 2024
概括
准确的狐尾小米水分含量检测对农业至关重要. 使用差分电容传感器的新Logistic-SSA-ELM算法提供了一种快速,非破坏性的实时质量评估和存储管理方法.
科学领域:
- 农业工程 农业工程
- 传感器和仪器仪表的使用
- 机器学习用于农业
背景情况:
- 湿度对于农产品的质量,加工和储存至关重要.
- 准确和快速检测狐尾小米中的水分有助于收获时间和质量控制.
- 现有的方法可能缺乏现代农业实践所需的速度或非破坏性.
研究的目的:
- 开发一种快速,非破坏性和准确的方法来检测狐尾小米的水分含量.
- 设计和实施差分电容传感器系统,用于实时测量.
- 优化机器学习模型,以提高水分含量预测准确度.
主要方法:
- 使用STM32和PCAP01芯片设计了一个差分电容传感器设备.
- 在不同的湿度,温度和体积功率循环条件下,对狐尾小米进行了容量测量.
- 逆向传播 (BP) 和极端学习机器 (ELM) 算法使用子搜索算法 (SSA) 进行了优化,并通过后勤算法 (Logistic-SSA-ELM) 进一步增强.
主要成果:
- 后勤-SSA-ELM算法证明了对狐尾小米水分含量的优异预测准确性.
- 关键绩效指标包括0.7016的预测设定确定系数 (RP),3.7150的RMSEP和1.4035.5的RPDP.
- 开发的电容传感器表现出良好的稳定性和高精度.
结论:
- 后勤-SSA-ELM模型为非破坏性的狐尾小米水分含量检测提供了出色的预测性能.
- 不同电容传感器系统是智能和精密农业实时监控的可行工具.
- 进一步的研究可以提高传感器的功能,用于更广泛的农业应用.
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