香束图像和视频数据集用于品种分类和分级
1Department of Studies in Computer Science, University of Mysore, Manasagangotri, Mysuru, Karnataka, 570006, India.
Data in brief
|April 15, 2025
概括
这项研究引入了一种新的香群级数据集,用于准确的品种识别和质量分级. 此资源有助于机器学习模型在食品加工行业.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 香是全球重要的水果作物,具有高营养价值和市场需求.
- 准确的香品种识别和质量分级对于食品加工行业至关重要.
- 现有的数据集缺乏全面的集群级数据,阻碍了准确的工业评估.
研究的目的:
- 为了弥补大规模,群级香数据集的差距.
- 为训练香分类和分级的机器学习模型提供一个有价值的资源.
- 为了支持食品加工行业提供准确的,集群级数据.
主要方法:
- 收集了三种香品种的群级图像和视频数据集:Elakki-bale,Pachbale和Rasbale.
- 专注于对工业加工和分级至关重要的集群级特征.
- 数据集来源于印度南卡尔纳塔卡州的迈苏鲁.
主要成果:
- 创建了一个全面的香品种图像和视频集群级数据集.
- 该数据集特别捕捉了与批发和食品加工业务相关的特征.
- 这个资源填补了现有的农业数据集中的关键缺口.
结论:
- 开发的数据集是机器学习应用在香品种分类和分级中的宝贵资源.
- 它通过专注于集群级数据,为食品加工行业提供了更准确的评估.
- 该数据集将促进农业技术和研究的进步.
关键词:
人工智能的人工智能是人工智能.香团级数据集数据集香的分类 香的分类香品种的分类 香品种的分类计算机视觉 计算机视觉 计算机视觉食品科学 食品科学 食品科学机器学习 机器学习精准农业 精准农业 精准农业更多相关视频
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