On the potential of Wireless Sensor Networks for the in-field assessment of bio-physical crop parameters

Autor(en): Bauer, J.
Siegmann, B.
Jarmer, T. 
Aschenbruck, N. 
Herausgeber: Aschenbruck, N. 
Kanhere, S.
Akkaya, K.
Stichwörter: Agricultural robots; Commercial off-the-shelf; Crops; User experience, Conventional approach; Global economies; Leaf Area Index; Limiting process; Simple approach; Situational awareness; Smart agricultures; Wireless sensor network (WSNs), Sensor nodes
Erscheinungsdatum: 2014
Herausgeber: IEEE Computer Society
Journal: Proceedings - Conference on Local Computer Networks, LCN
Volumen: 2014-November
Ausgabe: November
Startseite: 523
Seitenende: 530
The exploration of bio-physical crop parameters is fundamental for the efficiency of smart agriculture. The leaf area index (LAI) is one of the most important crop parameters and serves as a valuable indicator for yield-limiting processes. It contributes to situational awareness ranging from agricultural optimization to global economy. In this paper, we investigate the potential of Wireless Sensor Networks (WSNs) for the in-field assessment of bio-physical crop parameters. Our experiences using commercial off-the-shelf (COTS) sensor nodes for the indirect and nondestructive LAI estimation are described. Furthermore, we present the design of our measurement architecture and results of various in-field measurements. By directly comparing the results achieved by WSN technology with those of a conventional approach, represented by a widely used standard instrument, we analyze whether bio-physical crop characteristics can be derived from WSN data with a desired accuracy. Moreover, we propose a simple approach to significantly enhance the accuracy of COTS sensor nodes for LAI estimation while, at the same time, reveal open challenges. © 2014 IEEE.
Conference of 39th Annual IEEE Conference on Local Computer Networks, LCN 2014 ; Conference Date: 8 September 2014 Through 11 September 2014; Conference Code:112575
ISBN: 9781479937844
DOI: 10.1109/LCNW.2014.6927698
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84932649597&doi=10.1109%2fLCNW.2014.6927698&partnerID=40&md5=4f13f953817c7c595cb789591c874e35

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