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Towards a Low-cost RSSI-based Crop Monitoring

Authors:

Nils AschenbruckAuthors Info & Claims

ACM Transactions on Internet of Things, Volume 1, Issue 4

Article No.: 21, Pages 1 - 26

https://doi.org/10.1145/3393667

Published: 21 June 2020 Publication History

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Abstract

The continuous monitoring of crop growth is crucial for site-specific and sustainable farm management in the context of precision agriculture. With the help of precise in situ information, agricultural practices, such as irrigation, fertilization, and plant protection, can be dynamically adapted to the changing needs of individual sites, thereby supporting yield increases and resource optimization. Nowadays, IoT technology with networked sensors deployed in greenhouses and farmlands already contributes to in situ information. In addition to existing soil sensors for moisture or nutrient monitoring, there are also (mainly optical) sensors to assess growth developments and vital conditions of crops. This article presents a novel and complementary approach for a low-cost crop sensing that is based on temporal variations of the signal strength of low-power IoT radio communication. To this end, the relationship between crop growth, represented by the leaf area index (LAI), and the attenuation of signal propagation of low-cost radio transceivers is investigated. Real-world experiments in wheat fields show a significant correlation between LAI and received signal strength indicator (RSSI) time series. Moreover, influencing meteorological factors are identified and their effects are analyzed. Including these factors, a multiple linear model is finally developed that enables an RSSI-based LAI estimation with great potential.

References

[1]

Gregory Asner, Jonathan Scurlock, and Jeffrey Hicke. 2003. Global synthesis of leaf area index observations: Implications for ecological and remote sensing studies. Global Ecol. Biogeogr. 12, 3 (2003), 191--205.

[2]

Nouha Baccour, Anis Koubâa, Luca Mottola, Marco Antonio Zúñiga, Habib Youssef, Carlo Alberto Boano, and Mário Alves. 2012. Radio link quality estimation in wireless sensor networks: A survey. ACM Trans. Sensor Netw. 8, 4 (2012), 1--34.

Digital Library

[3]

Kenneth Bannister, Gianni Giorgetti, and Sandeep K. S. Gupta. 2008. Wireless sensor networking for “hot” applications: Effects of temperature on signal strength, data collection and localization. In Proceedings of the 5th Workshop on Embedded Networked Sensors (HotEmNets’08). ACM, 1--5.

[4]

Jan Bauer and Nils Aschenbruck. 2018. Design and implementation of an agricultural monitoring system for smart farming. In Proceedings of the IEEE IoT Vertical and Topical Summit for Agriculture (IOT’18). IEEE, 1--6.

[5]

Jan Bauer, Thomas Jarmer, Siegfried Schittenhelm, Bastian Siegmann, and Nils Aschenbruck. 2019. Processing and filtering of leaf area index time series assessed by in situ wireless sensor networks. Comput. Electron. Agric. 165 (2019).

[6]

Jan Bauer, Bastian Siegmann, Thomas Jarmer, and Nils Aschenbruck. 2016. On the potential of wireless sensor networks for the in situ assessment of crop leaf area index. Comput. Electron. Agric. 128 (2016), 149--159.

Digital Library

[7]

Beecham Research Ltd.2016. Enabling The Smart Agriculture Revolution—The Future of Farming through the IoT Perspective. Technical Report. Retrieved from http://www.beechamresearch.com/download.aspx?id=1051.

[8]

Carlo Alberto Boano, James Brown, Zhitao He, Utz Roedig, and Thiemo Voigt. 2010. Low-power radio communication in industrial outdoor deployments: The impact of weather conditions and ATEX-compliance. In Proceedings of the 1st International Conference on Sensor Applications, Experimentation, and Logistics (SENSAPPEAL’10). Springer, 159--176.

[9]

James Brinkhoff and John Hornbuckle. 2017. Characterization of wifi signal range for agricultural WSNs. In Proceedings of the 23rd Asia-Pacific Conference on Communications (APCC’17). IEEE, 1--6.

[10]

Qiang Chen, DaeHee Won, Dennis M. Akos, and Eric E. Small. 2016. Vegetation sensing using GPS interferometric reflectometry: Experimental results with a horizontally polarized antenna. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 9 (2016), 4771--4780.

[11]

Datasheet: CC2420. 2017. 2.4 GHz IEEE 802.15.4 / ZigBee-ready RF Transceiver. Technical Report. Texas Instruments. Retrieved from https://www.ti.com/lit/ds/symlink/cc2420.pdf.

[12]

Björn Gernert, Jan Schlichter, and Lars Wolf. 2019. PotatoScanner—A mobile delay tolerant wireless sensor node for smart farming applications. In Proceedings of the 15th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS’19). IEEE, Santorini Island, Greece, 106--113.

[13]

Ulrike Grömping. 2006. Relative importance for linear regression in R: The package relaimpo. J. Stat. Softw. 17 (2006), 1--27. Issue 1.

[14]

ITU-R. 2016. Recommendation P.833-9—Attenuation in vegetation. Technical Report. International Telecommunication Union (ITU). Retrieved from https://www.itu.int/rec/R-REC-P.833/en.

[15]

ITU-R. 2017. Recommendation P.618-13—Propagation Data and Prediction Methods Required for the Design of Earth-space Telecommunication Systems. Technical Report. International Telecommunication Union (ITU). Retrieved from https://www.itu.int/rec/R-REC-P.618/en.

[16]

Ulf Kulau, Stephan Rottmann, Sebastian Schildt, Johannes van Balen, and Lars Wolf. 2016. Undervolting in real world WSN applications: A long-term study. In Proceedings of the 12th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS’16). IEEE, 9--16.

[17]

Siyu Li and Hongju Gao. 2011. Propagation characteristics of 2.4 GHz wireless channel in cornfields. In Proceedings of the 13th IEEE International Conference on Communication Technology (ICCT’11). IEEE, 136--140.

[18]

Ji Luo, Xing Xu, and Qian Zhang. 2011. Understanding link feature of wireless sensor networks in outdoor space: A measurement study. In Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM’11). IEEE, 1--5.

[19]

Jari Luomala and Ismo Hakala. 2015. Effects of temperature and humidity on radio signal strength in outdoor wireless sensor networks. In Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS’15). IEEE, 1247--1255.

[20]

Manuel Campos-Taberner and Francisco Javier GarcA&eng;a-Haro and Gustau Camps-Valls and GonÃğal Grau-Muedra and Francesco Nutini and Alberto Crema and Mirco Boschetti. 2016. Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sens. Environ. 187 (2016), 102--118.

[21]

Ramona Marfievici, Amy L. Murphy, Gian Pietro Picco, Federico Ossi, and Francesca Cagnacci. 2013. How environmental factors impact outdoor wireless sensor networks: A case study. In Proceedings of the 10th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS’13). IEEE, 565--573.

[22]

Andrew Markham, Niki Trigoni, and Stephen Ellwood. 2010. Effect of rainfall on link quality in an outdoor forest deployment. In Proceedings of the International Conference on Wireless Information Networks and Systems (WINSYS’10). IEEE, 1--6.

[23]

Yonghua Qu, Yeqing Zhu, Wenchao Han, Jindi Wang, and Mingguo Ma. 2014. Crop leaf area index observations with a wireless sensor network and its potential for validating remote sensing products. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 7, 2 (Feb. 2014), 431--444.

[24]

Cassidy J. Rankine, G. Arturo Sanchez-Azofeifa, and Mike H. MacGregor. 2014. Seasonal wireless sensor network link performance in boreal forest phenology monitoring. In Proceedings of the 11th IEEE International Conference on Sensing, Communication, and Networking (SECON’14). IEEE, 302--310.

[25]

Jürgen Richter, Rafael F. S. Caldeirinha, Miqdad O. Al-Nuaimi, Andy Seville, Neil C. Rogers, and Nick Savage. 2005. A generic narrowband model for radiowave propagation through vegetation. In Proceedings of the 61st IEEE Vehicular Technology Conference (VTC’05), Vol. 1. IEEE, 39--43.

[26]

Siegfried Schittenhelm, Lorenz Kottmann, Martin Kraft, Katja Matschiner, and Tina Langkamp-Wedde. 2018. Agronomic performance of winter wheat grown under highly divergent soil moisture conditions in rainfed and water-managed environments. J. Agron. Crop Sci. 205, 3 (2018), 283--294.

[27]

Florian Schmidt, Matteo Ceriotti, Niklas Hauser, and Klaus Wehrle. 2015. If you can’t take the heat: Temperature effects on low-power wireless networks and how to mitigate them. In Proceedings of the 12th European Conference on Wireless Sensor Networks (EWSN’15). Springer, 266--273.

[28]

Kannan Srinivasan and Philip Levis. 2006. RSSI Is Under-appreciated. In Proceedings of the 3rd Workshop on Embedded Networked Sensors (EmNets’06). 1--5. Retrieved from https://sing.stanford.edu/site/publications/14.

[29]

John Thelen, Daan Goense, and Koen Langendoen. 2005. Radio wave propagation in potato fields. In Proceedings of the 1st Workshop on Wireless Network Measurement (WiNMee’05). IEEE, 1--5.

[30]

Deepak Vasisht, Zerina Kapetanovic, Jong-ho Won, Xinxin Jin, Ranveer Chandra, Ashish Kapoor, Sudipta N. Sinha, Madhusudhan Sudarshan, and Sean Stratman. 2017. Farmbeats: An IoT platform for data-driven agriculture. In Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI’17). USENIX Association, 515--529. Retrieved from https://www.usenix.org/system/files/conference/nsdi17/nsdi17-vasisht.pdf.

[31]

Hjalmar Wennerström, Frederik Hermans, Olof Rensfelt, Christian Rohner, and Lars-Åke Nordén. 2013. A long-term study of correlations between meteorological conditions and 802.15.4 link performance. In Proceedings of the 10th IEEE International Conference on Sensing, Communications and Networking (SECON’13). IEEE, 221--229.

[32]

Gaofei Yin, Aleixandre Verger, Yonghua Qu, Wei Zhao, Baodong Xu, Yelu Zeng, Ke Liu, Jing Li, and Qinhuo Liu. 2019. Retrieval of high spatiotemporal resolution leaf area index with gaussian processes, wireless sensor network, and satellite data fusion. Remote Sens. 11, 244 (2019), 1--18.

Cited By

Guerra RVizziello ASavazzi PGoldoni EGamba P(2024)Forecasting LoRaWAN RSSI using weather parametersComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110258243:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110258
Maheshwari AKumar Aggarwal ADanielescu A(2022)Designing Tools and Interfaces for Ecological Restoration: An Investigation into the Opportunities and Constraints for Technological InterventionsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517664(1-17)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517664

Index Terms

Towards a Low-cost RSSI-based Crop Monitoring

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Published In

cover image ACM Transactions on Internet of Things

ACM Transactions on Internet of Things Volume 1, Issue 4

November 2020

181 pages

EISSN:2577-6207

DOI:10.1145/3407671

Editors:
Schahram Dustdar
TU Wien, Austria
,
Gian Pietro Picco
University of Trento, Italy

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 21 June 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 January 2020

Received: 01 April 2019

Published in TIOT Volume 1, Issue 4

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Cited By

Guerra RVizziello ASavazzi PGoldoni EGamba P(2024)Forecasting LoRaWAN RSSI using weather parametersComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110258243:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110258
Maheshwari AKumar Aggarwal ADanielescu A(2022)Designing Tools and Interfaces for Ecological Restoration: An Investigation into the Opportunities and Constraints for Technological InterventionsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517664(1-17)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517664

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