EgyPLI:为埃及植物叶子识别提供现实生活注释图像数据集
Amany M Sarhan1,2, Mahmoud A Shaheen3
1Department of Computer and Control Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt. amany_sarhan@f-eng.tanta.edu.eg.
Scientific data
|February 6, 2026
概括
埃及植物叶子图像数据集 (EgyPLI) 提供了来自埃及的独特的现实植物叶子图像集合. 这一数据集有助于为各种农业应用开发可靠的自动化植物识别模型.
科学领域:
- 计算机视觉 计算机视觉
- 农业科学 农业科学
- 机器学习 机器学习
背景情况:
- 缺乏地理上多样化的数据集阻碍了通用的植物识别模型.
- 现有的数据集往往缺乏现实世界的可变性,限制了模型的稳定性.
- 埃及的农业部门需要自动化系统的本地化,代表性数据.
研究的目的:
- 介绍埃及植物叶子图像数据集 (EgyPLI) 作为一个新的资源.
- 为植物识别研究提供地理上多样化和现实的数据集.
- 促进开发强大的深度学习模型,用于植物分类和疾病诊断.
主要方法:
- 策划了来自八种植物的3588张真实世界叶子图像的数据集.
- 在不同的条件下 (视角,照明,杂乱) 捕捉到的图像,反映自然环境.
- 附注和预处理的图像,包括健康和生病的叶子,以实现一致的标准.
主要成果:
- 使用ResNet50,VGG16和自定义CNN评估数据集性能.
- 实现了高准确度:61.67% (ResNet50),96.81% (VGG16) 和99.22% (自定义CNN) 的数据.
- 证明了数据集适用于培训有效的植物识别模型的适用性.
结论:
- 埃及PLI解决了植物叶子图像数据集的关键缺口,特别是在埃及.
- 数据集的现实性支持训练可靠的模型进行实际部署.
- 埃及PLI能够在自动植物分类,诊断和健康评估方面取得进展.
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