薄荷叶:用于条件分析和机器学习应用的干燥,新鲜和坏的数据集
Rohini Jadhav1, Yogesh Suryawanshi2, Yashashree Bedmutha2
1Bharati Vidyapeeth College of Engineering, Pune, India.
Data in brief
|November 15, 2023
概括
一个由5323个薄荷叶图像组成的新数据集帮助机器学习进行质量评估. 该资源支持用于农业和工业应用的新鲜,干燥或腐烂的薄荷叶的识别研究.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业科学 农业科学
背景情况:
- 精确评估薄荷叶质量对于农业,食品保存和制药行业至关重要.
- 现有的叶子状况分析方法往往缺乏全面的,多样化的数据集,以进行强大的模型训练.
- 对于快速评估叶子质量的自动化系统的需求正在增加.
研究的目的:
- 为了介绍一个全面的数据集5,323薄荷 (pudina) 叶图像.
- 促进条件分析和机器学习研究,以评估叶子质量.
- 为培训和评估薄荷叶识别计算机视觉模型提供资源.
主要方法:
- 收集并策划了5323张薄荷叶图像的数据集.
- 图像代表着各种各样的状态:新鲜,干燥和受损.
- 包括手动注释,对每个图像进行分类,并确保灯光,背景和方向的变化.
主要成果:
- 一个多样化和注释的薄荷叶图像数据集现在可用.
- 数据集包括受控变异,以提高模型的通用性.
- 能够培训和评估机器学习和计算机视觉算法.
结论:
- 本文所介绍的数据集是推进自动化薄荷叶质量评估的宝贵资源.
- 它支持为需要快速质量评估的行业开发可靠的系统.
- 鼓励进一步研究用于分析工厂状况的创新机器学习方法.
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