Kriging has been shown to be the best interpolator to interpolate maps in precision agriculture. However, Kriging requires a high number of sampling points to generate accurate maps. Recently, machine learning (ML) techniques have shown the potential to produce maps with a lower number of sampling points. In addition, using ML map generation can be automated and use much more feature information to improve map quality. Therefore, the objective of this study was to implement a ML technique and compare it to IDW and to Ordinary Kriging (OK). The ML algorithm used was the Support Vector Machine (SVM). Software based on the SVM method was developed (Smart-Map) using the Python language. This software was tested in an area of 204 ha cultivated with soybeans. The performance of the SVM method was compared to traditional interpolation methods, IDW and Ordinary Kriging (OK). Based on the analysis of 10 soil attributes, OK had better performance than IDW and the ML method when the Moran’s I (Index) values were significant and higher than 0.67. With a low density of points and low degrees of spatial autocorrelation, the ML method performed better than IDW and OK.

克里金法已被证明是在精准农业中插值地图的最佳插值器。但是，克里金法需要大量的采样点才能生成准确的地图。最近，机器学习 (ML) 技术显示了生成具有较少采样点的地图的潜力。此外，使用 ML 地图生成可以自动化并使用更多的特征信息来提高地图质量。因此，本研究的目的是实施 ML 技术并将其与 IDW 和普通克里金法 (OK) 进行比较。使用的 ML 算法是支持向量机 (SVM)。开发了基于SVM方法的软件（Smart-Map) 使用 Python 语言。该软件在 204 公顷的大豆种植区域进行了测试。将 SVM 方法的性能与传统的插值方法、IDW 和普通克里金法 (OK) 进行了比较。通过对10个土壤属性的分析，当Moran's I（Index）值显着且高于0.67时，OK的性能优于IDW和ML方法。由于点密度低且空间自相关度低，ML 方法的性能优于 IDW 和 OK。