使用基于图像的振动响应分析,人工智能驱动的基维果坚固度预测
Seyedeh Fatemeh Nouri1, Saman Abdanan Mehdizadeh1, Yiannis Ampatzidis2
1Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz 63417-73637, Iran.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究开发了一种使用计算机视觉和机器学习的非破坏性系统,以评估果的坚硬性. 该方法通过分析振动引起的表面位移来准确预测度,为质量控制提供了可靠的替代方案.
科学领域:
- 农业工程 农业工程
- 生物物理学的生物物理.
- 计算机视觉 计算机视觉
背景情况:
- 非破坏性的果实坚度评估对于收获后的质量控制至关重要.
- 传统的方法可能是破坏性的和耗时的.
- 对果实成熟度的客观评估对于供应链管理至关重要.
研究的目的:
- 开发和验证基于图像的振动分析系统,用于非破坏性的基维果坚固度评估.
- 利用计算机视觉和机器学习来准确地预测度.
- 建立一种可靠的替代传统性测试方法.
主要方法:
- 120个子受到了受控的振动 (200-300赫兹).
- 数字摄像头用20秒的时间捕捉了表面的变化.
- 图像处理提取了减噪系数和自然频率.
- 一个神经网络模型使用这些动态特征预测了坚固性.
主要成果:
- 更坚固的子显示出更高的自然频率和更低的阻尼系数.
- 更柔软,成熟的水果表现出较低的自然频率和更高的阻尼.
- 神经网络在预测坚固度方面取得了很高的准确性 (R2=0.9951,RMSE=0.0185).
结论:
- 基于图像的振动分析系统可用于非破坏性坚固度评估.
- 该方法为水果质量评估提供了可靠和有效的替代方案.
- 该系统有可能实时实现自动分级和质量控制.
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