research-article

Soil Moisture Sensing with UAV-Mounted IR-UWB Radar and Deep Learning

Authors:

Xiaocheng Wang,

Chenghu ZhouAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 7, Issue 1

Article No.: 11, Pages 1 - 25

https://doi.org/10.1145/3580867

Published: 28 March 2023 Publication History

Abstract

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%.

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Index Terms

Soil Moisture Sensing with UAV-Mounted IR-UWB Radar and Deep Learning
1. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 7, Issue 1

March 2023

1243 pages

EISSN:2474-9567

DOI:10.1145/3589760

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Copyright © 2023 ACM.

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Publication History

Published: 28 March 2023

Published in IMWUT Volume 7, Issue 1

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Cited By

Wu ZXie FFang YLiang ZWan QXiong YCai X(2024)Seeing through the TactileProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596128:2(1-39)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659612
Liu CDong ZHuang LYan WWang XFang DChen X(2024)TagSleep3DProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435128:1(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643512
Wang ZGuo YRen ZSong WSun ZChen CGuo BYu Z(2024)LiqDetectorProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314437:4(1-24)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631443
Jacobs LAlipour MWatts ASoltanaghai E(2024)Joint Soil and Above-Ground Biomass Characterization Using RadarsIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS53475.2024.10640558(5235-5240)Online publication date: 7-Jul-2024
https://doi.org/10.1109/IGARSS53475.2024.10640558
Imbeault-Nepton TMaître JBouchard KGaboury S(2023)Fall Detection from UWB Radars: A Comparative Analysis of Deep Learning and Classical Machine Learning TechniquesProceedings of the 2023 ACM Conference on Information Technology for Social Good10.1145/3582515.3609535(197-204)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3582515.3609535

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