SpectroFood数据集:一个全面的水果和蔬菜超谱元数据集用于干物质估计
Ioannis Malounas1, Wout Vierbergen2, Sezer Kutluk3
1Agricultural University of Athens (AUA), Iera Odos 75, 11855 Athens, Greece.
Data in brief
|January 30, 2024
概括
这项研究引入了一个超光谱成像数据集,用于估计果,西兰花,,和中的干物质含量. 这些数据使人工智能模型能够在各种作物中非破坏性地预测干物质,从而改善模型的概括性.
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:
- 超光谱成像技术提供了作物属性的非破坏性分析.
- 对于跨不同作物的高光谱模型来说,一般化仍然是一个挑战.
- 准确的干物质估计对于作物质量评估至关重要.
研究的目的:
- 为多种作物提供一个全面的超谱数据集.
- 促进开发一个单一的人工智能模型用于干物质估计.
- 解决超光谱数据分析中的概括问题.
主要方法:
- 从果,西兰花,子和样本收集的高光谱数据 (430-900 nm).
- 使用校准的超光谱成像摄像机进行测量.
- 对1028个样本的提取平均反射频谱及其相应的干物质含量 (%) 被记录.
主要成果:
- 提供了1028个样本的数据集,包括光谱数据和干物质含量.
- 该数据集支持训练人工智能模型进行非破坏性干物质预测.
- 该方法旨在使单一模型能够跨不同作物类型工作.
结论:
- 这一数据集对于推进人工智能驱动的非破坏性作物分析具有价值.
- 这个资源可以提高超频谱模型的稳定性和适用性.
- 未来的研究可以利用这一数据集来开发更普遍的作物质量评估工具.
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