一个标记的图像数据集的常见的西红疾病的分类和对象检测的数据集
Yongbo Liu1, Yuhang Zhu2, Liang Hu1
1Institute of Agricultural Information and Rural Economy, Sichuan Academy of Agricultural Sciences, Chengdu 610011, China.
Data in brief
|October 27, 2025
概括
来自中国的新番茄病图像数据集有助于可持续农业. 这个资源支持人工智能驱动的作物疾病诊断,增强受保护的农业实践.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物病理学 植物病理学
背景情况:
- 准确的作物疾病诊断对于可持续农业至关重要.
- 高质量的图像数据集对于开发有效的疾病识别模型至关重要.
- 计算机视觉为保护农业的疾病检测提供了非侵入性方法.
研究的目的:
- 为了引入一种新的,高分辨率的番茄病图像数据集.
- 支持自动疾病分类和表型化研究.
- 促进深度学习模型的培训,用于作物健康监测.
主要方法:
- 在四川省的温室收集了1026张西红疾病 (病毒,灰,细菌) 的图像.
- 在现实环境下从多个角度和距离捕捉到的图像.
- 手动注释图像使用LabelImg,与专家指导,以XML格式存储数据.
主要成果:
- 数据集包含1026张图像 (2.78GB),详细说明了叶子,果实和茎.
- 图像捕捉了与现实世界的场景相关的多尺度表型疾病特征.
- 数据集包括病毒性疾病 (417),灰色菌 (82) 和细菌枯 (527) 的特定计数图像.
结论:
- 该数据集对人工智能驱动的疾病分类和对象检测研究有价值.
- 它支持深度学习模型培训和跨作物转移学习应用程序.
- 该资源通过增强的诊断技术,促进保护农业的可持续生产.
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