An interdisciplinary team held a bootcamp to develop farmer-oriented innovation projects using open-source technologies like Arduino and Raspberry Pi. Four projects were developed by farmers and students to monitor various agricultural data through IoT sensors and provide alerts or analyses to farmers. The bootcamp achieved its goals of enabling participants to innovate using affordable open-source tools and providing a model for future collaboration between farmers, students, and experts to develop precision agriculture solutions.

1. Farmer-oriented
innovation: outcomes
from a first bootcamp
Jérôme DANTAN, Davide RIZZO, Fatma FOURATI, Michel DUBOIS, Mehdi JABER
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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2. Farmer-oriented innovation:
outcomes from a first bootcamp
Jérôme DANTAN, Davide RIZZO, Fatma FOURATI, Michel DUBOIS, Mehdi JABER
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Corresponding
author
Presenting
today
Information
Technologies
Agronomy,
data science
Management
science
Agricultural
sciences expert
Digital
innovation
An interdisciplinary team
@pievarino

3. UniLaSalle
About us
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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4. Institut Polytechnique UniLaSalle
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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A private higher education establishment
2800 students
3 integrated degree
programs in Food &
Health, Geology &
Environment,
Agronomy and other
Bachelor and Master
degree programs
4 Academic and
Industrial Chairs
4 research groups
and several facilities member of the Lasallian education network
3 campuses in northern France:
Beauvais, Rouen, Rennes

5. About us
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Interdisciplinary research group
working on organizational innovation
Academic research and education team
adressing the innovation of agricultural
equipments and technology
Collaborative innovation centre for
agriculture based in Beauvais
(northern France)
© Davide Rizzo, 2018 CC BY-NC-SA

6. Background
& challenges
1 – Introduction
2 – Organization & main outcomes
3 – Take-home message
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.

7. Precision agriculture
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
Technologies are allowing
agriculture to be more
precise and smart.
The development of precision
agriculture and its support, the
digitalization of agriculture, is
leading to the emergence of
many new approaches that
combine agronomic
knowledge and agricultural
know-how with computer
skills.
7
©PETROSS,TERRA-MEPP&WEST,CC-BYhttps://flic.kr/p/XavvBA

8. Farm technology
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Important subset
of the agrifood
tech landscape,
often referred to as
‘agtech.’
Cosgrove E, Leclerc R, Burwood-Taylor L, Chauhan R (2018) AgFunder Agrifood Tech
Investing Report 2017. AgFunder
Farm Robotics, Mechanization &
Equipment. On-farm machinery,
automation, drone manufacturers,
grow equipment
Farm Management Software,
Sensing & IoT. Ag data capturing
devices, decision support software,
big data analytics
Novel Farming Systems, Ag
Biotechnology, Agribusiness
Marketplaces, Bioenergy &
Biomaterials, Farm-to-Consumer
eGrocery, etc.

9. Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
9©NestaCCBY-NC-SAhttps://www.nesta.org.uk/sites/default/files/future_farms_infographic_precision_agriculture.jpg

10. Breakthrough changes
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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AgTech and
precision farming
require farmers
to expand their
knowledge to be
able to master
recent farming
innovations.
https://ec.europa.eu/info/news/future-cap-whats-cooking-next-cap_en
“ The future Common Agricultural
Policy will be much more focused on
encouraging innovation (for
example through identifying where
there is a common need that
technology might be able to meet)
and on helping especially small and
medium-sized farms to join the
digital farming revolution.”
© xaviergp CC BY-NC https://flic.kr/p/EYLGYj

11. Bootcamp
1 – Introduction
2 – Organization & main outcomes
3 – Take-home message
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.

12. AgriLab® open innovation platform
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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The local territorial institutions have invested in the building
of AgriLab® to boost the sustainable and open innovation
of the AgTech sector in the Hauts-de-France region
© AgriLab

13. Some references for AgriLab®
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Do It Yourself: behaviors where
"individuals engage raw and semi-raw
materials and component parts to produce,
transform, or reconstruct material
possessions”1
FabLab: outreach project from MIT's
Center for Bits and Atoms (CBA), and
became into a collaborative and global
network of 1100 Fabrication Laboratories
Open Source Ecology: developing a set of
open source blueprints for the Global
Village Construction Set². Inspiring source
for the French Atelier Paysan
1 Wikipedia contributors, "Do it yourself," Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Do_it_yourself&oldid=831737917(accessed
1/4/18). ² https://www.opensourceecology.org/about-overview/
InforgraphicbyJBVervaeck,CCBY-SA©AtelierPaysan,CCBY-NC-SA

14. BootCamp goals and figures
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Enabling farmers and students in
agronomy to innovate by
themselves by using and
mastering the latest cheap and
open source technologies
Arduino, Raspberry Pi, Grove sensors,
LoraNetwork, 3D printing, etc.
4 farmers, 39 engineering students
in agronomy, 10 animators and
experts, and several observers
© Davide Rizzo, 2018 CC BY-NC-SA
2.5 days & >1000h of co-working

15. Bootcamp overview
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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http://agrilab.unilasalle.fr/projets/projects/open-iot-in-smartfarming
o Agricultural data: an answer
to what question?
(D. Rizzo)
o Farm Open-source DIY
Internet of Things
(M. Jaber)
o Developing affordable
open-source IoT
(C.Pham)
o IoT connectivity examples
with Wolfram Mathematica,
Arduino & Raspberry
(P. Fonseca)
o Data science et Human-
machine interface with
Wolfram tools
(D. Birraux)
Step 1
Step 2
morning
Step 3
afternoon
Step 4
morning
Step 5
afternoon
Step 6
14-16:30 pm Introductory conferences
17-18:30 pm Farmer-oriented teams
DAY#1DAY#2DAY#3
designing: defining variables to be
monitored and expected DSS interface
learning: electronic components,
packaging, Arduino connectivity and
HMI, early prototypes
assembling the concept
prototypes
wrap-up and perspectives

16. Open innovation approach
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Boosted by the Hauts-de-France Social
and Digital Innovation regional program
INS’Pir http://www.hautsdefrance.fr/inspir/
Everything documented in a free and
open knowledge base shared under
Creative Commons licence:
http://agrilab.unilasalle.fr/projets/projects/open-iot-in-smartfarming

17. Common project outline
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Physical variable(s)
Sensor(s) Arduino &
electronics
Network
(cloud)
Database
HMI, machine learning,
data vizualization
Icons by Icons8 https://icons8.com
From the farm and field issues to
the farmer’s decision making
Prototyping activity

18. 4 farmer-oriented projects
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Decisio
Soil moisture monitoring to:
o manage flax sowing
o identify potatoes harvesting
time
iPatate
o Potato stocks quality
monitoring
o Remote management of
agricultural activities
SiloTeam
o Remaining quantity of food in
poultry food storage silos
monitoring
o Comparative display of the
filling of silos
VegData
o Early rot detecting in salads
o Network farms data sensors
in the same region to achieve
large data collection to feed
artificial intelligence system
http://agrilab.unilasalle.fr/projets/projects/open-iot-in-smartfarming

19. Decisio
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Field Protective housing
Arduino
Lora network module
Smartphone
Sensor Battery
Issue
Specifications
Implementation
Physical data Output
Soil moisture
monitoring to
manage:
o flax sowing
o potatoes vegetation
Soil moisture and
temperature,
moisture and air
temperature, foliar
development and
rainfall
o Flax: knowing the best period
for sowing.
o Potatoes: predicting the
stoppage of vegetation
growth and thus the harvest
o Soil temperature and moisture
sensors, measured every 15 minutes
o Data storing in a cloud database
o HMI/Data visualization
o Alerts/warning SMS
o Web access
Decisio sensor-to-OAD package
http://agrilab.unilasalle.fr/projets/projects/open-iot-in-smartfarming

20. iPatate
Issue
Specifications
Implementation
Physical data Output
Potato stocks quality
monitoring
Temperature, CO2,
humidity and phyto-
hormones
Intervention of the
operator
o Retrieve and sort
data
o Data analyses to
send alerts
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Final version of the installation of
sensors and transmitters / receivers
within the storage building
http://agrilab.unilasalle.fr/projets/projects/open-iot-in-smartfarming

21. Vegdata
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Issue
Specifications
Implementation
Physical data Output
Early rot
detecting in
salads
Temperature of both
air and soil and
humidity of both air
and soil
Raising soil moisture to
warn the farmer when the
moisture reaches a
threshold that requires
irrigation
o Protection for sensors
o 3 temperature and Humidity
sensors (atmosphere and soil)
o HMI/data visualization
Location of sensors on the salad and underground
http://agrilab.unilasalle.fr/projets/projects/open-iot-in-smartfarming

22. SiloTeam
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Issue
Specifications
Implementation
Physical data Output
Monitoring and
quantifying the
poultry food
remaining in storage
silos
Height of food
remaining in the silo
Exact tonnage of
food remaining in
the silos
o Algorithm calculating the filling rate
of the silo, as well as a comparative
display of the filling of every silos of
the farm
o HMI/data visualization
Comparative display of the filling of silos
http://agrilab.unilasalle.fr/projets/projects/open-iot-in-smartfarming

23. Conclusions
1 – Introduction
2 – Organization & main outcomes
3 – Take-home message
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.

24. Opportunities & perspectives
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Future
challenges:
prototype Lora
network nodes and
gateways, deploy
Open TimeSeries
database platform,
putting all together
the four sensor
projects in the farms
o agronomy: enable farmers to
better monitor the
environment, thus to facilitate
the implementation of
precision farming
o economy: provide added
value for farmers, new
business models, startups
o technology: develop
predictive analyses, which
will require bigger data sets

25. Take home message
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Open innovative farmer-oriented
model can benefit from the
involvement of students and experts
in agronomy and in digitalization
technologies.
This approach to innovation leads to
a large range of new business
models, among which the creation
of a start-up becomes just one of
the multiple possibilities through
which the involved actors can
create new activities.
© Davide Rizzo, 2018 CC BY-NC-SA

26. Thank you! Any question?
Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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The Authors acknowledge the fundamental work done by the farmers & students who participated to the 1st Agrilab® bootcamp.
© AgriLab, 2017
Name.surname[at]unilasalle.fr @pievarinowww.unilasalle.fr

27. Farmer-oriented innovation: outcomes from a first bootcamp ● Dantan et al.
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Acknowledgements. The Authors acknowledge the fundamental work done by the farmers
and students who participated to the first Agrilab bootcamp. We wish to thanks Bernard De
Franssu - AgriLab® co-manager and director of the Sustainable Development Department at
UniLaSalle, Narges Bahi-Jaber - associate professor at UniLaSalle, Nathalie Schnuriger -
associate professor at UniLaSalle, Carolina Ugarte - associate professor at UniLaSalle, Pedro
Fonseca (SUEZ) and Dorian Birraux (Wolfram inc.) for their contribution to the design and
realization of the first bootcamp.
This work has been supported by Chaire Agro-Machinisme et Nouvelles Technologies, backed
by UniLaSalle with the financial support from the Michelin Corporate Fundation, AGCO
Massey-Ferguson, the Hauts-de-France Regional Council and the European Regional
Development Fund (ERDF).
Suggested citation: Dantan J, Rizzo D, Fourati F, Dubois M, Jaber M (2018) Farmer-oriented
innovation: outcomes from a first bootcamp. 3rd Abbé Grégoire Innovation Days, March 3rd
Paris.
Copyright: © 2018 Dantan et al. This is an open access article distributed under the terms of
the Creative Commons Attribution License CC-BY-NS-SA 4.0 that permits free use,
distribution, and reproduction in any medium, provided the original author and source are
credited. The use shall not be for commercial purposes and any derivate must keep the same
license as the original. https://creativecommons.org/licenses/by-nc-sa/4.0/
Source Availability Statement: the public bootcamp documentation of the is available on the
AgriLab® collaborative platform, accessible at : http://agrilab.unilasalle.fr/projets/projects/open-
iot-in-smartfarming
Design and layout: Davide Rizzo, April 3 2018