建立基于计算机视觉和机器学习的达库分级分类模型
Mengke Zhao1,2,3, Chaoyue Han1,2,3, Tinghui Xue1,2,3
1College of Food Science and Engineering, Shanxi Agricultural University, Taigu, Jinzhong 030801, China.
Foods (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了一种计算机视觉和机器学习模型,用于客观的Daqu分级,提高Baijiu质量评估的效率和准确性.
科学领域:
- 食品科学与技术 食品科学与技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 达奎等级显著影响州的质量.
- 目前的Daqu分级方法是主观的,劳动密集型和低效的.
研究的目的:
- 开发一个自动化,客观的Daqu等级评估系统.
- 为了提高Daqu对淡味Baijiu的分类效率和准确性.
主要方法:
- 用于Daqu图像提取的图像细分技术 (值,形态融合,K介质聚类).
- 使用随机森林平均减小精度 (RF-MDA),RFE,LASSO和回归的特征选择.
- 机器学习模型 (SVM,LR,RF,KNN,堆叠) 用于Daqu等级分类.
主要成果:
- 形态融合实现了高图像细分性能 (96.67%的准确性).
- 随机森林 (RF) 模型在分类Daqu-P,Daqu-F和Daqu-S方面表现出色 (96.67%的准确率).
- 结合RF-MDA和堆叠模型,在区分Daqu-P和Daqu-F方面表现优异 (90.00%的准确性).
结论:
- 拟议的计算机视觉和机器学习模型为Daqu等级评估提供了有效和客观的解决方案.
- 这种方法为Baijiu工业提供了宝贵的理论和技术支持.
- 自动化Daqu分级可以克服传统方法的局限性,确保一致的质量.
相关概念视频
01:26Classification of Systems-I
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
01:31Classification of Systems-II
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
01:29Aggregates Classification
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...