Abstract
Ground Penetrating Radar (GPR) has been widely studied as a tool for extracting soil parameters relevant to agriculture and horticulture. When combined with Machine-Learning-based (ML) methods, high-resolution Stepped Frequency Countinuous Wave Radar (SFCW) measurements hold the promise to give cost effective access to depth resolved soil parameters, including at root-level depth. In a first step in this direction, we perform an extensive field survey with a tractor mounted SFCW GPR instrument. Using ML data processing we test the GPR instrument's capabilities to predict the apparent electrical conductivity (ECaR) as measured by a simultaneously recording Electromagnetic Induction (EMI) instrument. The large-scale field measurement campaign with 3472 co-registered and geo-located GPR and EMI data samples distributed over ~6600 square meters was performed on a golf course. The selected terrain benefits from a high surface homogeneity, but also features the challenge of only small, and hence hard to discern, variations in the measured soil parameter. Based on the quantitative results we suggest the use of nugget-to-sill ratio as a performance metric for the evaluation of end-to-end ML performance in the agricultural setting and discuss the limiting factors in the multi-sensor regression setting. The code is released as open source and available at this https URL.
Abstract (translated)
透地雷达（GPR）作为一种用于提取与农业和园艺相关的土壤参数的工具，已经得到了广泛研究。当与机器学习（ML）方法相结合时，高分辨率逐次频移连续波雷达（SFCW）测量具有将成本有效地获取到深度解析土壤参数（包括根层深度）的潜力。在朝着这个方向迈出的第一步中，我们使用搭载拖拉机上的SFCW GPR仪器进行了一场广泛的现场调查。利用ML数据处理，我们测试了GPR仪器的功能，以预测同时记录电磁感应（EMI）仪器测量的表面电导率（ECaR）。在一片高尔夫球场地上进行的这项大规模现场测量活动使用了3472个共同注册和几何定位的GPR和EMI数据样本，覆盖约6600平方米。所选地面得益于高表面均匀性，但同时也存在测量土壤参数小且难以分辨的挑战。根据定量结果，我们建议将泥炭到胎里的比率作为衡量整个ML性能的性能指标，并讨论了多传感器回归设置中的限制因素。该代码是开源的，您可以在此链接https上获取。
URL
https://arxiv.org/abs/2404.15961
