一个新的数据集的枣果检查和分类的数据集
Abdul Khalique Maitlo1, Riaz Ahmed Shaikh1, Rafaqat Hussain Arain1
1Institute of Computer Science, Shah Abdul Latif University Khairpur, Pakistan.
Data in brief
|January 23, 2024
概括
引入了一套新的,全面的枣果图像数据集,以应对手工分类的挑战. 该资源有助于开发用于枣果分类和质量检查的自动化系统.
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:
- 手动的日期水果分类是劳动密集型,容易出现错误,并受到高劳动力流动的影响.
- 质量控制受到损害,导致枣行业的水果浪费和价格波动.
- 现有的机器学习和深度学习模型需要精心策划的数据集,以便有效地对果进行分类.
研究的目的:
- 引入一种新的,土著的数据集,用于枣果的分类和分类.
- 促进开发智能系统,用于自动化枣果检查.
- 支持日加工行业的可持续经济增长.
主要方法:
- 创建一个数据集,包括3004个预处理图像的四种枣果品种.
- 图像按尺寸 (大,中,小) 和质量等级 (1, 2, 3) 分类.
- 数据集组织成18个目录,用于研究可访问性.
主要成果:
- 这里展示了一个标准化数据集,包含3004张枣果图像.
- 该数据集包括基于大小和质量等级的详细分类.
- 组织结构有利于研究人员立即使用.
结论:
- 这一数据集是推进自动化枣果分类系统的宝贵资源.
- 它可以显著减少人工劳动,最大限度地减少水果浪费,稳定市场价格.
- 该倡议支持水果加工部门的经济发展.
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