LuffaFolio:一个多维图像数据集的光滑的LuffaFolio
Md Ripon Sheikh1, Md Masudul Islam1,2, Galib Muhammad Shahriar Himel1,3,4,5
1Department of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh.
Data in brief
|February 21, 2024
概括
研究人员现在可以使用新的全面图像数据集对Luffa疾病进行分类,评估Luffa等级,并识别生长阶段. 这一资源是为了支持农业研究和农作物管理,以帮助顺的Luffa (Luffa Aegyptiaca).
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 计算机视觉 计算机视觉
背景情况:
- 对于农业生产率来说,对Luffa疾病的准确分类和质量至关重要.
- 现有的数据集可能缺乏对Luffa Aegyptiaca (Smooth Luffa) 在疾病,等级和生长阶段等各个方面的全面覆盖.
- 基于图像的分析为农业监测提供了一个可扩展的方法.
研究的目的:
- 为了引入一个全面的,多方面的图像数据集为Luffa Aegyptiaca.
- 促进对Luffa疾病分类,质量分级和生长阶段识别的研究.
- 为了支持对Luffa作物管理的自动化系统的开发.
主要方法:
- 一套1933年JPG图像的数据集从孟加拉国法里德普尔的村庄田里收集.
- 数据集分为三个部分:Luffa疾病 (1228张图片),花朵 (362张图片) 和Luffa等级 (343张图片).
- 图像捕捉了各种Luffa疾病 (例如,Alternaria,角斑,马赛克病毒),花的成熟阶段和Luffa质量 (新鲜或有缺陷).
主要成果:
- 该数据集提供了各种各样的Luffa图像集合,这些图像被分类为特定的农业研究任务.
- 它包括对常见的Luffa叶病的详细分类以及鲜明的花朵生长阶段.
- 在Luffa等级部分提供图像,以区分新鲜和有缺陷的产品.
结论:
- 这一数据集是推动Luffa Aegyptiaca种植和管理研究的宝贵资源.
- 它可以开发和验证用于自动化Luffa分析的机器学习模型.
- 数据集的全面性质支持植物病理学和农业质量评估中的多种应用.
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