无接触声波振动方法用于多种桃子种植品种的坚固度评估
Dachen Wang1,2,3, Laili Li3, Tao Shi3
1Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China.
Foods (Basel, Switzerland)
|November 27, 2025
概括
声波振动技术与深度学习相结合,提供了一种非破坏性的方法来评估桃子的坚性. 这种先进的方法显著提高了工业质量控制和跨种应用的准确性.
科学领域:
- 农业工程 农业工程
- 食品科学 食品科学 食品科学
- 机器学习 机器学习
背景情况:
- 桃子的坚度对质量至关重要,但在工业环境中很难以非破坏性方式测量.
- 传统方法具有破坏性,限制了它们在批量检测和实时质量评估中的使用.
研究的目的:
- 开发和评估一种非接触方法,用于评估多种品种的桃子度.
- 为了比较深度学习模型的性能与固度预测的传统回归技术.
主要方法:
- 在机械刺激后,使用激光多普勒振动计非侵入性捕获声学振动反应.
- 频域光谱被用作部分最小平方回归 (PLSR),支持向量回归 (SVR) 和一个新的1D卷积神经网络 (ISNet-1D) 的输入.
- ISNet-1D集成了Inception模块和挤压和激发功能提取和优先级的增强.
主要成果:
- 在一个独立的测试组中,ISNet-1D模型实现了卓越的预测准确性 (RP2=0.8069,RMSEP=0.9206 N/mm,RPDP=2.2879).
- 深度学习模型的性能归因于它使用多尺度过器和注意力机制优先考虑歧视性光谱特征的能力.
- 一个分层的转移学习策略增强了不同桃子品种的模型通用性.
结论:
- 无接触声学振动与深度学习 (ISNet-1D) 结合,提供了一种强大而准确的方法,用于非破坏性的桃子坚固度评估.
- 这种方法显示了工业应用的巨大潜力,包括各种桃子品种的自动分类和质量控制.
- 开发的方法为水果行业实时质量评估提供了实用且具有成本效益的解决方案.
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