精确的二维视觉解决方案用于估计梨的物理特征
Hieu M Tran1,2, Tuan M Le1,2, Ke Wang2
1School of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City, Viet Nam.
Scientific reports
|October 9, 2025
概括
弗鲁斯图姆方法使用几何学准确估计梨质量,优于回归模型. 这为需要精确分类和包装的农业行业提供了具有成本效益的解决方案.
科学领域:
- 农业工程 农业工程
- 食品工程 食品工程
- 计算机视觉 计算机视觉
背景情况:
- 对质量等物理性质的准确估计对于高效的农业和食品加工业务至关重要.
- 目前用于估计梨质量的方法可能缺乏精度或需要大量数据和复杂的计算.
- 用于分类,称重和包装的自动化系统需要可靠的预测模型.
研究的目的:
- 系统地审查和比较基于几何和基于回归模型的梨质量估计技术.
- 用手动收集的横截面数据评估不同方法的准确性和可靠性.
- 为有效和成本效益的农业和食品工程应用确定最佳建模方法.
主要方法:
- 基于几何 (Frustum方法) 和基于回归的技术 (Ridge, LASSO,弹性网,线性回归,MLP回归器,梯度增强回归器) 的系统审查和比较.
- 使用手动收集的横截面梨数据进行模型培训和验证.
- 在回归模型中采用超参数优化和K折交叉验证,以确保可靠性并最大限度地减少过拟合.
主要成果:
- 弗鲁斯图姆的方法表现出卓越的性能,在20个切片中实现了4.24%的根平均平方百分比误差 (RMSPE) 和3.43%的平均绝对百分比误差 (MAPE).
- 回归模型,特别是回归模型,表现出强的表现,平均RMSPE为4.30%,MAPE为3.52%,在15个切片的5个折叠中表现出强的表现.
- 模型准确估计了梨的尺寸 (宽度和长度),误差低于1.53%和模型匹配参数超过99%.
结论:
- 弗鲁斯图姆方法是精确估计梨质量的强大可靠技术,不需要大型数据集或复杂的计算.
- 回归模型提供了具有竞争力和稳定的替代方案,其中回归是显着的表现者.
- 这些发现支持在精密农业和智能食品加工中实施自动化技术,包括机器人收获和分类.
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