用计算机视觉和整体机器学习进行工厂质量控制的子的非破坏性体积估计
Wattanapong Kurdthongmee1, Arsanchai Sukkuea1
1School of Engineering and Technology, Walailak University, 222 Thaibury, Thasala, Nakorn Si Thammarat 80160, Thailand.
Journal of imaging
|October 28, 2025
概括
本研究介绍了一种使用机器学习和计算机视觉的非破坏性方法,以准确预测色体积. 这种方法加强了农产品的工业质量控制.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的体积估计对于工业质量控制至关重要,特别是在食品和农业部门.
- 传统的体积估计方法可能耗时且具有破坏性.
- 开发非破坏性,精确的体积预测技术对于高效的质量评估至关重要.
研究的目的:
- 利用机器学习和计算机视觉开发一种全面的,非破坏性的方法来预测色体积.
- 创建一个可靠的管道来估计水果尺寸和预测体积.
- 加强农业中的工业质量控制过程.
主要方法:
- 使用校准标记器对子的顶部和侧面视图来估计四个关键维度.
- 超出基本几何学的工程特征,包括面积与体积的比率和基于形状的描述符.
- 在150个子的数据集上训练和微调一个机器学习模型,特别是一个堆积回归器.
主要成果:
- 堆积回归器模型获得了0.971的高R2得分,超过了LightGBM等单一模型基准.
- 该方法通过依赖基本的物理特征,证明了对水果变化的稳定性.
- 该方法可以适应各种产品类型,表明其广泛适用性.
结论:
- 开发的方法为色体积预测提供了精确而非破坏性的解决方案.
- 该技术支持实时密度计算,用于在工厂设置中自动检测缺陷和质量分级.
- 该研究强调了先进的计算机视觉和机器学习在农业质量控制中的潜力.
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