Soil Moisture Sensing with UAV-Mounted IR-UWB Radar and Deep Learning
Soil Moisture Sensing with UAV-Mounted IR-UWB Radar and Deep Learning
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DOI：
10.1145/3580867
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发表时间：
2022-03
影响因子：
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通讯作者：
Rong Ding;Haiming Jin;Dong Xiang;Xiaocheng Wang;Yongkui Zhang;Dingman Shen;Lu Su;Wentian Hao;Ming Tao;Xinbing Wang;Cheng Zhou
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文献类型：
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作者：
Rong Ding;Haiming Jin;Dong Xiang;Xiaocheng Wang;Yongkui Zhang;Dingman Shen;Lu Su;Wentian Hao;Ming Tao;Xinbing Wang;Cheng Zhou
大面积土壤湿度传感是智能灌溉系统的关键要素。然而，现有的土壤水分传感方法通常无法同时获得令人满意的流动性和高水分估计精度。在本文中，我们提出了一种新的土壤水分传感系统，命名为SoilId，结合无人机和COTS IR-UWB雷达大面积土壤水分传感，而不需要埋在任何电池供电的地面设备的设计和实现。具体而言，我们设计了一系列新颖的方法来帮助SoilId从接收到的雷达信号中提取土壤水分相关特征，并自动检测和丢弃受无人机不可控运动和多径干扰污染的数据。此外，我们利用深度神经网络强大的表示能力，精心设计了一个神经网络模型，将提取的雷达信号特征准确地映射到土壤湿度估计。我们已经广泛评估了SoilId对各种现实世界的因素，包括无人机的不可控运动，多径干扰，土壤表面覆盖，以及许多其他。具体来说，我们的无人机系统进行的实验结果验证，土壤ID可以推动基于RF的土壤水分传感技术的精度限制为0.23%的50%分位数MAE。
Wide-area soil moisture sensing is a key element for smart irrigation systems. However, existing soil moisture sensing methods usually fail to achieve both satisfactory mobility and high moisture estimation accuracy. In this paper, we present the design and implementation of a novel soil moisture sensing system, named as SoilId, that combines a UAV and a COTS IR-UWB radar for wide-area soil moisture sensing without the need of burying any battery-powered in-ground device. Specifically, we design a series of novel methods to help SoilId extract soil moisture related features from the received radar signals, and automatically detect and discard the data contaminated by the UAV's uncontrollable motion and the multipath interference. Furthermore, we leverage the powerful representation ability of deep neural networks and carefully design a neural network model to accurately map the extracted radar signal features to soil moisture estimations. We have extensively evaluated SoilId against a variety of real-world factors, including the UAV's uncontrollable motion, the multipath interference, soil surface coverages, and many others. Specifically, the experimental results carried out by our UAV-based system validate that SoilId can push the accuracy limits of RF-based soil moisture sensing techniques to a 50% quantile MAE of 0.23%.