豆豆梅的图像数据集用于梅成熟度检测
Riccosan1, Soni Yora1, Nur Azmi Karim2
1Computer Science Department, School of Computer Science, Bina Nusantara University Bandung Campus, Jakarta, 11480 Indonesia.
Data in brief
|June 27, 2025
概括
这项研究引入了一种自动化方法,用于使用机器学习和图像检测来监测米发酵. 这种方法通过分析大豆发酵阶段来提高传统食品生产的效率.
科学领域:
- 食品科学与技术 食品科学与技术
- 农业工程 农业工程
- 计算机科学 (机器学习)
背景情况:
- 印度尼西亚传统的发酵大豆产品佩 (Tempeh) 是全球重要的动物蛋白替代品.
- 目前的培生产依赖于手工观察,在1-4天的发酵过程中进行手工观察.
- 手动过程是劳动密集型的,为自动化提供了提高效率的机会.
研究的目的:
- 探索计算机图像检测与机器学习的潜力,以自动化在培生产中观察大豆发酵.
- 开发一组图像数据集,捕捉传统梅发酵的关键阶段.
- 通过技术整合,提高培制造业的工作时间效率.
主要方法:
- 图像数据集的开发重点是传统米豆的豆发酵过程.
- 在三个不同的发酵阶段使用智能手机摄像头获取图像数据:Day-0,Day-1和Day-2.
- 数据预处理包括图像过和裁剪,图像被组织成基于时间的文件.
主要成果:
- 一个精心策划的图像数据集,代表了大豆发酵过程的视觉进展.
- 对于每个发酵阶段 (Day-0,Day-1,Day-2) 确定了独特的视觉特征.
- 用基于机器学习的图像分析来监测发酵的潜在潜力.
结论:
- 计算机图像检测和机器学习为自动化梅发酵监测提供了可行的解决方案.
- 创建的图像数据集作为开发自动化质量控制系统的基础.
- 自动化可以显著提高传统培生产的效率和一致性.
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