Accelerating Data-Driven Agriculture - with Wireless Soil Moisture Sensors Colleen Josephson () - Stanford Platform Lab
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Accelerating Data-Driven
Agriculture
with Wireless Soil Moisture Sensors
Colleen Josephson (cajoseph@stanford.edu)
Nov. 2019 1
Mass fish deaths in Australia’s Darling River (2019)
Theewaterskloof Dam in 2018
near Cape Town, South Africa
2
https://phys.org/news/2019-01-sea-white-hundreds-thousands-fish.html
Nearly 70% of fresh water is used to grow food... Aquastat, Water withdrawal by sector, Sep 2014, http://www.globalagriculture.org/fileadmin/files/weltagrarbericht/AquastatWithdrawal2014.pdf. 3
Projected to reach
~10 billion by 2050
4
Precision agriculture
Using data to make decisions
about water, fertilization, etc.
20-50% water savings via soil moisture sensors while yield
maintained or improved…but
Reasons for lack of sensor adoption
1.) High sensor cost 2.) Difficulty of deploying + maintaining
The average sensor is > $100 Wires, weather and watts
Teros-12 capacitive soil sensor: $225
A weatherproofed soil moisture sensor
box from the Geosensor Networks Lab
3.) Difficulty collecting + processing data
Fields don’t have WiFi
6
How do we get moisture sensing to go mainstream?
Make the system cheaper and easier to install
and maintain by pairing underground RFID-like
tags with a centralized radar reader.
tag1 tag2
Reasons for lack of sensor adoption
1.) High sensor cost 2.) Difficulty of deploying + maintaining
The average sensor is > $100 Wires, weather and watts
Teros-12 capacitive soil sensor: $225
A weatherproofed soil moisture sensor
box from the Geosensor Networks Lab
3.) Difficulty collecting + processing data
Fields don’t have WiFi
8
1. Intro
2. Background
○ Overview of current sensing technologies
○ Sensing using RF
3. Design
4. Considerations
5. Evaluation
6. Limitations + Opportunities
9
How do sensors approximate soil moisture?
● Volumetric water content (WVC), defined as:
● Dielectric permittivity, ε, is ability of a substance to
hold electrical charge
● Relative permittivity (sometimes dielectric constant):
εr = ε/ε0
● εr changes as water content of soil changes →
G. C. Topp, J. L. Davis, and A. P. Annan. 1980. Electromagnetic determination of soil water content: Measurements in coaxial 10
transmission lines. Water Resources Research 16, 3 (Jun 1980), 574–582. https://doi.org/10.1029/wr016i003p00574Common commercial sensors
Capacitive Time Domain Reflectometry (TDR)
● Measures charge time of a ● RF slowdown of 2-6x in soil
capacitor ● TDR sends pulse down prongs
● Roughly linear function of measures time it takes to return
permittivity ● εr ≅ (cτ/d)2, where d is length of prongs
● Less prone to corrosion than and τ is time of flight
resistive sensors ● Retails for $300-1000+ USD
● Retails for $100-300 USD
11Measuring time of flight
VS
12Jean-Jacques DeLisle , What’s the Difference Between Broadband and Narrowband RF Communications?, 2014, 13 https://www.mwrf.com/systems/what-s-difference-between-broadband-and-narrowband-rf-communications
Soil Sensing Using Wi-Fi
Jian Ding and Ranveer Chandra, MobiCom '19
● Senses soil moisture using MIMO WiFi
● Drawbacks:
○ Requires burying multiple antennas in
soil with wires to a laptop
○ Limited WiFi chips give access to
necessary info
○ Somewhat extensive calibration
14Backscatter
The scattering of radiation or particles back towards the source
15Ultra-wideband
radar
Transceiver measures ToF to
surface and underground tag
Tags buried at known depthBackscatter tags
● Passive, e.g. RFID
○ Harvests power via RF and uses backscatter
communication
● Semi-passive, e.g. Hitchhike/Freerider
○ Chip powered by battery, but uses
backscatter communication
● Active, e.g. toll transponders
○ Chip powered by battery, but uses
amplified backscatter communication
0dB reference signal transmitted to
antenna buried 30cm under soil
[1] Pengyu Zhang, Dinesh Bharadia, Kiran Joshi, and Sachin Katti.2016. Hitchhike: Practical backscatter using commodity wifi. In ACM SEN- SYS.
[2] Pengyu Zhang, Colleen Josephson, Dinesh Bharadia, and Sachin Katti. 2017. FreeRider. In Proceedings of the 13th International Conference
on emerging Networking EXperiments and Technologies - CoNEXT 17. ACM Press. https: //doi.org/10.1145/3143361.3143374 17Backscatter tags
● Passive, e.g. RFID
○ Harvests power via RF and uses backscatter
communication
● Semi-passive, e.g. Hitchhike/Freerider
○ Chip powered by battery, but uses
backscatter communication
● Active, e.g. toll transponders
○ Chip powered by battery, but uses amplified
backscatter communication
0dB reference signal transmitted to
antenna buried 30cm under soil
[1] Pengyu Zhang, Dinesh Bharadia, Kiran Joshi, and Sachin Katti.2016. Hitchhike: Practical backscatter using commodity wifi. In ACM SEN- SYS.
[2] Pengyu Zhang, Colleen Josephson, Dinesh Bharadia, and Sachin Katti. 2017. FreeRider. In Proceedings of the 13th International Conference
on emerging Networking EXperiments and Technologies - CoNEXT 17. ACM Press. https: //doi.org/10.1145/3143361.3143374 18Backscatter tags
● Passive, e.g. RFID
○ Harvests power via RF and uses backscatter
communication
● Semi-passive, e.g. Hitchhike/Freerider
○ Chip powered by battery, but uses
backscatter communication
● Active, e.g. toll transponders
○ Chip powered by battery, but uses
amplified backscatter communication
0dB reference signal transmitted to
antenna buried 30cm under soil
[1] Pengyu Zhang, Dinesh Bharadia, Kiran Joshi, and Sachin Katti.2016. Hitchhike: Practical backscatter using commodity wifi. In ACM SEN- SYS.
[2] Pengyu Zhang, Colleen Josephson, Dinesh Bharadia, and Sachin Katti. 2017. FreeRider. In Proceedings of the 13th International Conference
on emerging Networking EXperiments and Technologies - CoNEXT 17. ACM Press. https: //doi.org/10.1145/3143361.3143374 19SNR of tag prototypes
20Semi-passive
prototype
21Can we make it passive?
● “Mud batteries” harvest power from
microbes in the soil
● Harvests an avg. of 36uW on our drip
irrigated farm
● Current sensor consumes 116uW,
but could reduce to 35uW using
different components
● Open questions: will passive design
work? Can we add an amplifier?
[1] Lin, Fu-To, et al. "A self-powering wireless environment monitoring system using soil energy." IEEE Sensors Journal 15.7 (2015): 3751-3758. 22Isolating the backscatter signal
● Target is object of interest (i.e. tag)
● Clutter is reflections from all objects
that are not the target
● Underground is extremely cluttered
● Need to separate clutter from the
target to measure ToF
How? Make the tag seem like it’s moving
The reflection from the tag is difficult to discern
among clutter here
23Radars divide the field of view into range bins:
For a radar with one TX and one RX antenna:
R_frame = [a1eb1j, a2eb2j, …, aN-1ebN-1j, aNebNj]T
If the radar captures a total of P frames, we get a complex N x P matrix:
R_capture = [R_frame1, R_frame2, …, R_frameP-1, R_framePz]
24Applying a 1-D FFT to each range bin across all P pulses, we get another N x P
matrix where the magnitude of the rows are like a PSD for that range bin:
fft_capture = [ ,
,
distance (range bins)
,
]
25
frequency (hz)Range-Doppler plot of IDFT(R_capture)
Aliased
harmonics
105 Hz backscatter
26Fundamental frequency vector from range-Doppler matrix
SNR gained from oscillating tag
The oscillating tag successfully counteracts clutter, increasing the SNR
manyfold compared to a non-oscillating tag 28The long-term vision
[S]mallholder farms operate on 12% of the world's agricultural land and
produce 80% of the food that is consumed in Asia/sub-Saharan Africa
[The] developing world [has] 98.7 per cent mobile
phone adoption (as of 2017)
59% of the world owns a smartphone
[1] https://www.cropscience.bayer.com/en/crop-science/smallholder-farming
[2] https://www.theregister.co.uk/2017/08/03/itu_facts_and_figures_2017/ 29
[3] http://www.pewglobal.org/2018/06/19/2-smartphone-ownership-on-the-rise-in-emerging-economies/1. Intro
2. Background
3. Design
4. Considerations
○ How deep to deploy sensors
○ Types of agricultural soil
5. Evaluation
6. Limitations + Opportunities
30Sensing depth
● 70% of water absorbed from top half of
roots
● 15-45 cm for plants, up to 75 for trees
● OUR GOAL: minimum of 30cm
● Other radar approaches limited to 10-20cm
[1] USDA. 1997. National Engineering Handbook Irrigation Guide.
31
[2] Rana, Surinder. (2011). Principles and Practices of Soil Fertility and Nutrient Management. 10.13140/RG.2.2.30430.02888.SNR vs tag depth for 3 radars
32SNR vs tag depth in situ Measurements come from 100s captures performed in an actively watered farm field containing sandy clay loam. The VWC of the soil was about 15% at the time of measurement. 33
Soil types
34
USDA Soil Textural Triangle https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/edu/kthru6/?cid=nrcs142p2_0543111. Intro
2. Background
3. Design
4. Considerations
5. Evaluation
○ Laboratory
○ In situ
6. Limitations + Opportunities
35Ultra-wideband radar
Experiment
setup
Buried
prototype
36Active tag VWC measurements
The tag was buried at a depth of 30cm. Moisture level 1 is completely dry soil which was then gradually
dampened in 7 liter increments until saturation at level 5. Note that saturation depends on the soil type.
37Semi-passive tag VWC measurements
The tag was buried at a depth of 30cm. Moisture level 1 is completely dry soil which was then gradually
dampened in 4.5 liter increments until the signal was undetectable within 100 seconds of integration.
38In situ experiments
39Active/semi-passive VWC
Experiments were performed on a
farm field of sandy clay loam.
Measurements were taken every 30
minutes, with 7 liters of water poured
on the soil at times 2, 4, 6 and 8.
401. Intro
2. Background
3. Design
4. Considerations
5. Evaluation
6. Limitations + Opportunities
41Low cost ✓
Easy installation ✓
Maintainability ✓
Scalability ✗
Robustness ?
Do the readings stay valid if
the soil surface changes or
vegetation occludes LOS?Use relative ToF in second
Do version of prototype
the readings stay valid if
the soil surface changes or
vegetation occludes LOS?Additional opportunities for future work
● Measure electrical conductivity (EC)
○ Associated with salinity and fertilizer levels,
important data to farmers
● Non-ag radar backscatter (e.g. self-driving
cars)
● Scaling to large farms: drones?
○ Planning optimal flight paths (e.g. Farmbeats)
○ Combining aerial imagery with sensor data
○ Can drones fly low enough/stationary enough?
○ Synthetic aperture radar→
44
Vasisht, Deepak et al. “FarmBeats: An IoT Platform for Data-Driven Agriculture.” NSDI (2017).Ag is one of tech’s final frontiers
● This system just one small part of solutions
● New kinds of data at unprecedented volume and variety
● Pressing need for systems to be mobile friendly
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