Abstract The use of aerial remote sensing platforms such as Unmanned Aircraft Systems (UAS) has been proven as a cost and time effective way to perform tasks related to precision agriculture and decision making. Two machine learning (ML) algorithms have been implemented on UAS blue and red band imagery to delineate field areas and extents of various bare soil fields: the Random Forest non-parametric supervised classifier and the unsupervised non-parametric Mean Shift clustering algorithm. Both ML algorithms perform exceptionally well. The mean Area Goodness of Fit (AGoF) for bare soil areas was greater than 99% and the mean Boundary Mean Positional Error (BMPE) was lower than 0.6 m for the 11 surveyed fields. Such precisions with ML algorithms will enable an easy automated field boundary delineation based on UAS imagery. Such information is needed by growers and crop insurance agencies for an accurate determination of the crop insurance premiums.

中文翻译：

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使用均值漂移无监督聚类和随机森林监督分类从无人机系统图像中划定裸露土壤区域

摘要 使用无人机系统 (UAS) 等航空遥感平台已被证明是执行与精准农业和决策相关的任务的成本和时间有效的方法。两种机器学习 (ML) 算法已在 UAS 蓝带和红带图像上实现，以描绘各种裸土田地的田地面积和范围：随机森林非参数监督分类器和无监督非参数均值漂移聚类算法。两种 ML 算法都表现出色。11 个调查田的裸土区域的平均面积拟合优度 (AGoF) 大于 99%，平均边界平均位置误差 (BMPE) 低于 0.6 m。机器学习算法的这种精度将使基于 UAS 图像的简单自动场边界划定成为可能。