一个全面的综合数据集关于和茶叶病的图像,用于分类
Md Masum Billah1, Saifuddin Sagor1, Mohammad Shorif Uddin2
1Computer Science & Engineering, Daffodil International University, Dhaka, Bangladesh.
Data in brief
|January 26, 2026
概括
一个新的数据集结合了Hibiscus和茶叶图像,用于检测植物疾病. 一个ConvNeXt-Tiny模型实现了96%的准确性,帮助智能农业和植物病理学研究.
科学领域:
- 植物病理学 植物病理学
- 计算机视觉 计算机视觉
- 农业技术 农业技术
背景情况:
- 准确的植物疾病鉴定对于作物产量和粮食安全至关重要.
- 现有的数据集通常集中在单一物种上,限制了更广泛的模型适用性.
- 开发强大的分类模型需要多样化和注释良好的数据.
研究的目的:
- 为了创建一个结合花和茶叶树叶图像的数据集.
- 训练和评估一个深度学习模型来分类植物叶状况和疾病.
- 将数据集,模型和脚本公开用于研究.
主要方法:
- 收集了花和茶叶的高分辨率图像,涵盖了各种疾病和健康状态.
- 应用了广泛的数据增强来平衡数据集,从1413个原始图像中获得了13000个增强图像.
- 微调了ConvNeXt-Tiny深度学习模型在增强数据集上的叶子状况分类.
主要成果:
- ConvNeXt-Tiny模型在分类两种物种的叶子状况方面取得了96%的整体准确性.
- 证明了微调模型的强大性能和高分辨力.
- 结合的数据集促进了对各种植物叶病理的有效模型培训.
结论:
- 将多种植物物种组合到单一数据集中可以提高植物疾病分类的模型通用性.
- ConvNeXt-Tiny模型有效地识别各种叶子状况,并具有高精度.
- 公开可用的资源支持智能农业,植物病理学和计算机视觉应用的进步.
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