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一个广泛的图像数据集,用于以深度学习为基础的孟加拉国大米核品种的分类
Md Tahsin1, Md Mafiul Hasan Matin1, Mashrufa Khandaker1
1Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Data in brief
|December 5, 2024
概括
来自38个品种的76000张高分辨率大米图像的新数据集有助于农业研究. 这种全面的图像收集支持育种计划和研究大米遗传多样性的研究.
科学领域:
- 农业科学 农业科学
- 遗传学 遗传学 是一个
- 计算机视觉 计算机视觉
背景情况:
- 了解大米的遗传多样性对于作物改进至关重要.
- 现有的数据集可能缺乏高级分析所需的分辨率或多样性.
- 农业研究所之间的合作增强了研究能力.
研究的目的:
- 引入当地大米品种的全面高分辨率图像数据集.
- 支持大米遗传学,育种和农业实践方面的研究.
- 利用先进的成像和数据增强来丰富农业数据.
主要方法:
- 开发一个数据集,包括38种当地大米品种.
- 获取19,000个原始高分辨率显微镜图像.
- 应用数据增强技术 (缩放,旋转,照明) 来生成57,000张额外的图像,总共为76,000张图像.
主要成果:
- 一个丰富的,增强的数据集,包括76000张图像,详细介绍了38种大米品种.
- 图像捕捉到独特的特征,如颜色,尺寸和农业效用.
- 该数据集通过增强模拟各种环境条件.
结论:
- 该数据集为分析大米遗传多样性提供了宝贵的资源.
- 它促进了创新的农业实践和育种计划的发展.
- 这种资源使研究人员能够有效地研究和利用大米遗传资源.
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