INSTITUTE OF COMPUTER SCIENCE

Distributed Systems Group

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WSN4Crop
In-Situ Wireless Sensor Networks for Agricultural Crop Monitoring

Architecture of CAN't

In the context of Precision Agriculture, continuous monitoring of plant parameters plays an important role. IEEE 802.15.4 Wireless Sensor Networks (WSNs) are tailored for such in-situ monitoring systems. They have the potential to record the condition of cultivated plants and transforming it into up-to-date parameter maps, which can identify potential growth- and yield-reducing factors at an early stage. Thereby, WSNs enable decision support that contributes to a site-specific, targeted, and sustainable management of agricultural fields.

In the project WSN4Crop, a automated agricultural monitoring is realized that enables a cost-efficient in-situ assessment of an important plant parameter, the Leaf Area Index (LAI). To this end, based on commercially available WSN hardware, a low-cost sensor prototype for a passive, transmission-based LAI assessment is designed and experimentally evaluated in various field campaigns (cf. Publication 2). Tailored to the specific application, a network architecture is designed that transfers the prototype into a holistic long-term monitoring system (cf. Publication 3). Finally, the potential of the implemented monitoring system for a continuous and temporally high-resolution LAI acquisition is analyzed using exemplarily realized deployments and the empirical data sets (cf. Publication 4).

Realted Project: Smart fLAIr.

For questions and suggestions, please contact Jan Bauer.


Publications

  1. Jan Bauer, Bastian Siegmann, Thomas Jarmer, Nils Aschenbruck
    "Poster: Towards in-situ Sensor Network assisted Remote Sensing of Crop Parameters"
    Poster at the ACM International Symposium on Mobile Ad Hoc Networking and Computing
    MobiHoc, Paderborn, Germany, July 4-8, 2016. [pdf]
  2. Jan Bauer, Bastian Siegmann, Thomas Jarmer, Nils Aschenbruck
    "On the Potential of Wireless Sensor Networks for the In-Situ Assessment of Crop Leaf Area Index"
    Elsevier Computers and Electronics in Agriculture, Vol. 128, Oct. 2016, pp. 149-159. [pdf]
  3. Jan Bauer, Nils Aschenbruck
    "Design and Implementation of an Agricultural Monitoring System for Smart Farming"
    Proc. of the IEEE Internet of Things Vertical and Topical Summit for Agriculture
    Monteriggioni (Siena) Tuscany, Italy, May 8-9, 2018. [pdf]
  4. Jan Bauer, Thomas Jarmer, Siegfried Schittenhelm, Bastian Siegmann, Nils Aschenbruck
    "Processing and Filtering of Leaf Area Index Time Series Assessed by In-Situ Wireless Sensor Networks"
    Elsevier Computers and Electronics in Agriculture, Vol. 165, Article 104867, Oct. 2019. [pdf]
  5. Jan Bauer, Nils Aschenbruck
    "Towards a low-cost RSSI-based Crop Monitoring"
    ACM Transactions on Internet of Things (TIOT), Vol. 1 (4), Article 21, June 2020. [pdf]
Publications with regard to a related smartphone application for LAI assessment can be found here.


Data set

The data set contains raw sensor data and additional logging information from a certain long-term deployment at experimental fields at the Institute for Crop and Soil Science of the Julius Kühn-Institut (JKI) in Braunschweig, Germany. This data includes readings from light, temperature and humidity sensors as well as link quality information (RSSI and LQI) and collected during the deployment. Further details can be found in the README.