一个多式成像数据集用于加拿大野生大米粒的质量分级,使用RGB和VNIR高光谱数据
Yinka Sikiru1, Chyngyz Erkinbaev1
1Department of Biosystems Engineering, University of Manitoba, E2-376, EITC, 75A Chancellor's Circle, Winnipeg R3T 5V6 Manitoba, Canada.
Data in brief
|March 9, 2026
概括
一个新的多式联网数据集结合了RGB图像和加拿大野生大米 (Zizania palustris) 质量评估的超光谱数据. 此资源有助于研究自动化谷物分类和使用机器学习检测缺陷.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 频谱学是一种光谱学.
背景情况:
- 收获后对谷物的质量评估对于粮食安全和贸易至关重要.
- 传统的谷物质量评估方法往往是劳动密集型和主观的.
- 开发用于谷物质量表征的自动化,客观方法是一个正在进行的研究领域.
研究的目的:
- 介绍一个新的加拿大野生大米 (Zizania palustris) 核的多式联络数据集.
- 促进使用集成成像技术进行自动化收获后质量评估的研究.
- 支持开发用于谷物分类和缺陷检测的机器学习模型.
主要方法:
- 获取高分辨率的RGB图像和可见近红外 (VNIR) 超光谱反射率数据,从单个野生大米核中获取.
- 将RGB图像标准化为512 × 512像素的补丁,并进行数据增强.
- 处理VNIR高光谱数据,包括放射性校正,内核细分和光谱谱提取.
- 种子的种类分为八个质量级别:健康 (大/中等),变色 (低/高),破碎 (低/高),受虫害和未剥皮.
主要成果:
- 一个全面的多式联通数据集,包括对联的RGB图像和VNIR高光谱数据,用于8个不同的野生大米品质类别.
- 原始和加工数据的可用性,包括反射率校正的超立方体,细分面具,光谱配置文件和元数据.
- 证明数据集对多式联网机器学习应用程序的有用性.
结论:
- 呈现的多式联运数据集是推进自动化谷物质量评估的宝贵资源.
- 整合RGB和超光谱数据为缺陷检测和分级提供了增强的能力.
- 这一数据集将促进计算机视觉,光谱学和农业应用机器学习方面的创新.
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