可可豆的多模态超谱数据集与物理化学注释
Kebin Contreras1, Mohamad Jouni2, Mauro Dalla Mura2,3
1Universidad Industrial de Santander, 68002, Bucaramanga, Colombia.
Scientific data
|November 27, 2025
概括
这项研究引入了用于非侵入性可可豆质量评估的新光谱数据集. 这些数据使发酵水平的自动分类成为可能,提高了客观性和可扩展性.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 数据科学数据科学数据科学
背景情况:
- 传统的可可质量评估是主观和破坏性的.
- 有限的光谱数据集阻碍了客观的,非侵入性的质量评估.
- 对于可扩展的方法,需要一个全面的数据集.
研究的目的:
- 以各种空间光谱分辨率呈现可可豆光谱属性的新型数据集.
- 为了实现非侵入性,客观的可可质量评估.
- 支持开发可可豆质量的自动分类方法.
主要方法:
- 在开放和封闭条件下使用四个成像设备获取了19个场景.
- 收集的物理化学数据 (水分,多,) 和高光谱图像 (350-1000 nm).
- 按照哥伦比亚标准NTC 1252:2021进行了豆发酵分类 (好,部分,差).
主要成果:
- 该数据集成功地使用光谱信息来区分可可质量级别.
- 在侵入性和非侵入性条件下都证明了分类的准确性.
- 统计分析支持自动化质量评估工具的开发.
结论:
- 这一数据集对于推进非侵入性可可豆质量评估具有价值.
- 超光谱成像与机器学习相结合,可以客观地分类可可发酵.
- 这项工作为可可行业提供了可扩展和客观的方法.
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