机器学习应用程序的基于知识的标签的自动杂草细分
Thuan Ha1, Kathryn Aldridge2, Eric Johnson2
1Department of Plant Sciences, University of Saskatchewan, Saskatoon, S7N5A8, Canada. thuan.ha@usask.ca.
Scientific reports
|January 26, 2026
概括
一个自动化工作流程准确地标记了用于精密农业的无人机 (UAV) 图像中的景观特征. 这减少了为机器学习杂草检测创建数据集的手工工作.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 准确的景观特征分类对于精准农业至关重要,包括杂草控制和可变率应用.
- 机器和深度学习模型为实时杂草检测提供了潜力,但需要广泛的标记数据集,这些数据集需要大量的劳动力来创建.
研究的目的:
- 开发和评估用于无人机 (UAV) RGB 图像的自动化功能标签工作流.
- 为了减少用于农业机器学习应用程序生成标记数据集所需的手工工作和时间.
主要方法:
- 使用eCognition软件 (v9.5) 开发了一个自动化工作流程,集成了细分,线路检测,距离映射,卷积过,形态操作和值.
- 使用高分辨率无人机RGB图像 (0.088厘米) 来研究一个研究领域的各种杂草物种 (kochia,野麦,野末,假剪刀) 和小麦.
- 植被指数,包括植被颜色指数和过量绿色指数,用于区分作物,杂草和土壤.
主要成果:
- 在没有人工培训标签的情况下,自动化工作流实现了87%的整体准确性,卡帕系数为0.81,用于对景观特征进行分类.
- 空间算法和植被指数的组合有效地将作物和杂草从土壤背景中分离出来.
- 随机分布的标签点和一个混矩阵被用于性能评估.
结论:
- 开发的自动化工作流显示了加速创建用于机器学习和深度学习的标记数据集的巨大潜力.
- 这种方法可以大大减少手工劳动,同时保持用于杂草检测和其他精准农业任务的高分类准确性.
- 未来的研究将专注于提高工作流的跨领域的可转移性,获取日期和实验条件.
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