一个数据集彻底改变了印度湾叶分析的数据集
Priyanka Paygude1, Sandip Thite2, Ajay Kumar3
1Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.
Data in brief
|November 18, 2024
概括
一个新的印度湾叶图像数据集有助于质量评估. 这个资源支持机器学习来验证香料的真实性,并改善印度香料行业.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 食品科学 食品科学 食品科学
背景情况:
- 印度海湾叶对美食至关重要,但质量和真实性经常受到损害.
- 需要自动化质量评估来确保香料的完整性.
研究的目的:
- 介绍一个全面的,印度海湾叶样本的高分辨率图像数据集.
- 促进叶子状况分析和用于质量评估的机器学习方面的研究.
主要方法:
- 收集了5696张印度海湾叶样本的高分辨率图像.
- 在受控条件下捕捉到的图像,其中包括照明,背景和叶子方向的变化.
- 将样品分类为新鲜,干燥和易患病的情况.
主要成果:
- 开发了数字印度湾叶数据集,一个多样化和标准化的资源.
- 该数据集涵盖了各种叶子状况和成像参数.
- 为开发海湾叶质量的机器学习模型提供了基础.
结论:
- 数字印度湾叶片数据集是研究人员的宝贵资源.
- 它将加速香料自动化质量评估的进展.
- 旨在通过提高真实性和质量控制来增强印度整体香料行业.
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