Design and Implementation of an Agricultural Monitoring System for Smart Farming
Jan Bauer, Nils Aschenbruck University of Osnabrück
Institute of Computer Science Distributed Systems Group
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© John Deere Precision Farming • crop yield optimization • precise management, incl.
o fertilization o irrigation o identification of crop diseases
Demand for situational awareness • How to determine and monitor
crop parameters?
Motivation: Agriculture is changing…
© Claas
© Claas
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Leaf Area Index (LAI) • indicator for photosynthetic performance and yield-limiting processes • key variable for models in agronomy, climatology, ecology, …
Motivation: Bio-Physical and Bio-Chemical Crop Parameters
LAI = green leaf area
ground surface area
fractional cover
biomass fPAR
leaf chlorophyll content
OSNBR NDVI
LAI
Crop Parameters
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Basic Concept: Smart fLAIr in a Nutshell
Methodology • radiation-based LAI estimation • Ambient Light Sensor (ALS) • above (A) and below (B) canopy light measurements LAI derived from light transmittance (Monsi and Saeki, 1953)
𝐿𝐿𝐿 = −𝜔 ∗ 𝑙𝑙𝐵��̅�
LAI = 0 LAI = 1 LAI = 3 LAI = 0 LAI = 1 LAI = 3
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Sens4Crop
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.
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Smart fLAIr
Jan Bauer, Bastian Siegmann, Thomas Jarmer, Nils Aschenbruck "Smart fLAIr: a Smartphone Application for Fast LAI Retrieval using Ambient Light Sensors" Proc. of the IEEE Sensors Application Symposium, SAS, Catania, Italy, April 20-22, 2016.
Idea • above + below light measurements
via smartphone • Ambient Light Sensor (ALS)
optional: sensor accessory, (i.e., diffuser and band-pass filter)
Prototype • Samsung Galaxy Nexus (GT-i9250) • Android 4.4.4 Kitkat (not rooted!)
Lars Hunning, Jan Bauer, Nils Aschenbruck "A Privacy Preserving Mobile Crowdsensing Architecture for a Smart Farming Application" accepted for the ACM Workshop on Mobile Crowdsensing Systems and Applications in conjunction with the 15th ACM Conference on Embedded Networked Sensor Systems, SenSys, Delft, The Netherlands, Nov. 5-8, 2017.
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Sensor data transport & IoT Integration
Farm /
FMIS sender receiver
Internet
802.15.4
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Architectural Overview
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MQTT Broker
Farm / FMIS
IoT Gateway
User
Cluster n
G G
G G
RPi
Inte
rnet
PL
NM
W
LAN
W
SN
Cluster n+1
G G
G G
RPi R R
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Architectural Overview
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Imag
ery
© G
oogl
e
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Realization: Technical Aspects
Distributed setup WSN Application • Platform: TelosB • PAR Sensor: Hamamatsu S1087 • Software: TinyOS
Connectivity • Raspberry Pi as Wifi Gateway • Alix UMTS Router for Internet Access
Data Collection • PAR (+ temperature, humidity) • measurement every 2 minutes
• sampling rate: 3 Hz • 10 samples
• centralized LAI processing
Abov
e (A
) sen
sors
Below (B) sensor
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Impressions of harsh outdoor impacts on WSN equipment
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Impressions of harsh outdoor impacts on WSN equipment – Shit happens
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Exemplary time series - Temperature and humidity of two days (single sensor)
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Exemplary time series PAR measurements of a Cluster
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Exemplary time series – LQI values captured by a cluster head
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University of Osnabrück School of Mathematics/Computer Science
Distributed Systems Group
Wachsbleiche 27 D-49090 Osnabrück, Germany Phone: +49 541 969 2396 [email protected] http://sys.cs.uos.de
Prof. Dr. Nils Aschenbruck
Thanks for your attention!
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© 2018 Institut für Informatik, Universität Osnabrück