灰植物叶片图像的数据集用于检测和分类
Nusrat Jahan1, Md Zahid Hasan1
1Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka-1216, Bangladesh.
Data in brief
|September 8, 2025
概括
这项研究介绍了灰数据集,其中包括来自孟加拉国的灰 (Benincasa hispida) 植物的2676张图像. 该数据集有助于对植物卫生条件进行分类,以提高农业生产率.
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:
- 灰 (Benincasa hispida) 是孟加拉国的重要蔬菜,对营养和经济至关重要.
- 了解灰植物健康对于提高农业生产率至关重要.
- 有限的数据集存在,用于记录孟加拉国灰植物疾病.
研究的目的:
- 创建和呈现一套关于灰植物健康状况的综合数据集.
- 为了促进机器学习模型的开发,用于灰植物的疾病分类.
- 支持用于识别和管理影响灰种植的疾病的研究.
主要方法:
- 从孟加拉国达卡的不同地区收集了2676张灰植物的原始图像.
- 将图像分为五个类别:健康,虫,菌,叶子卷和叶子矿工.
- 用803个健康样本和1873个患病样本对数据集进行了结构化.
主要成果:
- 灰黄瓜数据集包括五个不同的健康类别的2676张图像.
- 该数据集适合在分析和机器学习模型培训中灵活使用.
- 为研究灰植物疾病提供了基础资源.
结论:
- 灰数据集是农业研究和疾病管理的宝贵资源.
- 允许更深入的分析和机器学习模型的发展,用于灰植物疾病的分类.
- 通过更好地了解植物健康,有助于提高农业生产率.
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