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Updated: Jun 3, 2025

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灰色层特征与深度转移学习的结合,用于使用机器学习和神经网络进行copra分类
A Stephen Sagayaraj1, T Kalavathi Devi2
1Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India. snafia.sagayaraj@gmail.com.
Scientific reports
|January 10, 2025
概括
这项研究开发了一种新方法,使用图像分析和机器学习从通常干燥的铜中分类硫化铜. 基于神经网络的模式识别实现了99.6%的准确性,有利于买家.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 图像处理 图像处理
背景情况:
- 干子 (copra) 对于石油和副产品生产至关重要.
- 传统的太阳干燥和工业硫化对铜质量产生影响.
- 对买家来说,准确地对类型进行分类至关重要.
研究的目的:
- 开发和评估一种可靠的方法来分类硫化铜与通常干燥的铜.
- 通过准确的质量评估,提高透明度,使农和买家受益.
主要方法:
- 从干燥工业收集和细分的铜图像.
- 结合灰色级共同发生矩阵 (GLCM) 特性与转移学习模型特性.
- 使用机器学习分类器和神经网络评估的特征集.
主要成果:
- 基于神经网络的模式识别 (NNPR) 实现了最高的准确性 (99.6%),灵敏度 (99.64%),特异性 (99.64%),F1-Score (99.6) 和卡帕得分 (0.99).
- 其他分类器,如随机森林 (98.9%准确度),物流回归 (98.3%) 和KNN (98.3%) 也显示出高性能.
- 拟议的方法明显优于现有的科普拉分类文献.
结论:
- 开发的方法提供了一个高度准确和可靠的方法来分类硫化铜.
- 这种分类系统为铜行业的利益相关者提供了实际的实用性.
- 该研究表明,将GLCM和转移学习功能结合起来,以图像为基础的铜质量评估的有效性.
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