通过计算机视觉和深度学习来预测和可视化Tencha干燥过程中的水分含量
Jie You1, Dengshan Li1, Zhen Wang2
1School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China.
Journal of the science of food and agriculture
|February 13, 2024
概括
本研究介绍了一种计算机视觉和深度学习方法,用于监测卡干燥. 该方法准确地预测了水分含量,使得卡生产中能够更好地控制质量.
科学领域:
- 农业工程 农业工程
- 食品科学 食品科学 食品科学
- 计算机视觉 计算机视觉
背景情况:
- 精确的水分含量监测对于卡的质量至关重要.
- 目前的方法是主观的,破坏性的,或缺乏可复制性.
- 需要客观的,非破坏性的方法来处理Tencha.
研究的目的:
- 开发一款计算机视觉和深度学习模型,用于在州进行非破坏性水分含量检测.
- 为了比较不同预处理技术和机器学习模型的有效性.
- 为了可视化查干燥过程中的水分分布.
主要方法:
- 在干燥过程中获取Tencha样本的图像.
- 提取和预处理 (MinMax,Z分数) 的色彩空间组件.
- 开发和比较1D-CNN,PLS和BP ANN模型用于湿度预测.
主要成果:
- 使用Z分数预处理的1D-CNN模型实现了高预测精度 (Rp = 0.9548).
- 成功可视化了水分迁移的空间和时间分布.
- 计算机视觉和1D-CNN证明了实时湿度预测的可行性.
结论:
- 计算机视觉与1D-CNN相结合,提供了一种可行且准确的方法来预测查湿度.
- 这项技术为智能化和工业化Tencha生产提供了技术支持.
- 这些发现有助于改善Tencha制造中的质量控制和流程优化.
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