一个无人机图像数据集用于对象检测,用LabelImg和Roboflow生成的注释
Anindita Das1, Vinitha Hannah Subburaj1, Yong Yang1
1West Texas A&M University, Canyon, TX 79016, USA.
Data in brief
|February 12, 2026
概括
这一无人机图像数据集有助于精准农业和机器学习用于作物杂草检测. 它支持自动化监测和可持续农业,在农业中推进人工智能.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 精准农业需要精确的作物杂草差异化,以有效管理.
- 用于杂草检测的机器学习模型需要大型,多样化的数据集进行培训和验证.
研究的目的:
- 为了介绍一套新的无人机图像数据集从棉花田.
- 促进对象检测模型的开发,以区分作物和杂草.
- 建立一个基准来评估人工智能模型在农业环境中的性能.
主要方法:
- 在各种棉田条件下获取高分辨率无人机图像.
- 对图像进行注释,以识别和标记作物和杂草实例.
- 数据集策划以确保其质量和适合机器学习任务.
主要成果:
- 一个全面的数据集,使作物杂草检测的 robust 模型训练成为可能.
- 一个标准化资源,用于对不同AI算法的比较分析.
- 改善自动化农业监测系统的基础.
结论:
- 发布的数据集对于推进人工智能驱动的精密农业至关重要.
- 它通过加强杂草管理来支持可持续发展的农业实践的发展.
- 本资源为农业和环境管理中人工智能应用的更广泛领域做出贡献.
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