iGreen: Co-creative Mobile Services in Agriculture - A Knowledge Management Perspective

Andreas
Dengel

© German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 1

Agenda

Some
words
about
the
world‘s
food
situations

The
iGreen
project

Agricultural
knowledge
management
–
two

perspectives

Consolidating
knowledge
and
decision
support

services

Crop
forecasting

Logistic
planning

Soil
quality
map
adjustment

Summary
and
conclusions


Agriculture
must
provide
food
for
an
extra
2
billion
people
but
80%
of

extra
agricultural
food
can
be
provided
only
through
yield
increase!

9
billion
people
Extra
agricultural
food
production

by
2050!
100%

≤20%
≥80%

By
increasing
arable

By
increasing
yield

land

Amount
of
arable
land
Too
many
factors

in
developed
countries
High
complexity!

World
demand
for
cereal
(bn
tonnes2
(mn
hectares)

?

)

+43%
-‐0.2%

625

3,0
575

2,1

2010
2050
2010
2050

Source:
(1)
UN,
World
population
to
exceed
9
billion
by
2050.
http://www.un.org/esa/population/publications/wpp2008/pressrelease.pdf,
2008

(2)
N.
Alexandratos,
J.
Bruinsma,
G.
Boedeker,
J.
Schmidhuber,
S.
Broca,
P.
Shettym,
and
MG
Ottaviani.
World
agriculture:
towards
2030/2050.

Interim
Report.
Prospects
For
Food,
Nutrition,
Agriculture
and
Major
Commodity
Groups,
2006.


IT
can
help
to
improve
and
economize
methods,
procedures
and
agricultural

technologies
to
produce
more
eﬃciently
and
save
resources

¨  Precision
farming:

Linked
and
controlled
by
GPS,
seed,
fertilizer
and

pesticides
are
yielded
exactly
where
they
are
needed

¨  Digital
soil
quality
maps:

Continuous
measure
lead
to
updated
guidelines
helping

to
plan
the
supply
of
nutrients
or
predict
harvest

¨  Analysis
of
high-‐resolution
images
from
space:

Controlled
specialized
satellites
take
pictures
of

agricultural
region
all
over
the
world

¨  Field
robots
will
take
over
some
agricultural
tasks:

Equipped
vehicles
(not
only
for
harvesting)
are
staﬀed

with
sensors
to
measure
the
state
of
crop
and
soil


What
are
the

ideas
of
the
iGreen
project?


iGreen
builds
on
an
alliance
of
23
partners
from
science,
business
and
public

institutions

Connec4vity

private
public

(interna'onal)

(na'onal)

iis

(regional)

EPP (na4onwide)

e.v.

Interests
of
the
agricultural
sector
Interests
of
the
social
community

Applica4on
Consul'ng
services

Das Bildelement mit der
Beziehungs-ID rId7
Management

wurde in der Datei nicht

Farmers
/
agricultural
service

gefunden.

supply
agency


iGreen
strikes
a
new
path
in
public-‐private
knowledge
management
having
a

focus
in
agriculture

¨  The
goal
of
iGreen
is
to
develop
location-‐bases
services
and
knowledge
sharing

networks
for
combining
distributed,
heterogeneous
public
as
well
as
private

information
sources

¨  Built
on
that,
we
aim
at
the
development
of
mobile
decision
assistants
using
Web

services
for
a
decentralized
support
of

energy-‐eﬃcient,
economic,

environmental-‐adapted
and
collaboratively-‐organized

production
and
planning
processes

¨  There
are
many
high-‐potential
application
ﬁelds
for
the
iGreen

platform,
such
as
agriculture,
forestry,
water
supply
and

distribution,
urban
development
and
landscaping,
or
nature

conservation

¨  in
iGreen
we
exemplary
focus
on
crop
production


In
crop
production
there
is
whole
bunch
of
relevant
questions
having
a
space-‐
time
relationship
that
have
to
be
answered
for
making
decisions

Which
kind
of
plant
species

When
should
we
apply
what

should
we
cultivate

kind
of
fertilizer

and
to
which
dose?

at
what
location?

How
much
pesticides
When
is
the
best

should
we
apply
at
time
for
the
harvest?

what
time?

What
market
prices

may
we
expect?


Public
country-‐wide
institutions
in
Germany
act
as
competence
centers
and

provide
best-‐practice
advice
about
crop
production
for
farmers

Competence

Center

Support
for
Data
Management
Compendium
about

and
Model
Generation

Best-‐Practice
Crop
Protection

Advise
in
Grade
and
Seed
Regional
Data

Selection
About
Fields
and
Lots

Consulting
about
Fertilizer
Data
Base
about
Crop
Experiment

Combination
and
Use
Results

Tools
for
Prognoses
and
News
about
Market
Development

Statistical
Data
Analysis
Information
about
how
to
combat

pest
and
plant
diseases

(best
time,
conditions
and
strategies)


Some
examples
about
the
services
from
the
competence
centers

Competence

Center

Pest Immigration
Data Interpolation Methods (MR, Kriging, spline, ....)

2009 2011

Exposition Gradient Regional Risc Map
Factor Weather Station Information (Point⇒Zone)

+ + =
⇒ ⇒


Yield
depends
on
many
factors
–
Experts
and
Decision
Support
Systems

help
farmers
to
optimize
yields

Ecological

Social
factors

factors

Yield

•  Soil
quality
•  Transfer
of
skills

and
roles

•  Weather

condition
•  Security
of
land

(rainfall)
tenure

•  Sun
exposition
•  Access
to
land

?

Economical
Political

factors
factors
+
Ecological
factors

•  Fuel,
fertilizers,
•  Agricultural
+
Economical
factors

pesticides
prices
subventions
+
Political
factors

•  Product
selling
•  Quotas
+
Social
factors

prices
(Market)
+
Model(s)

•  Other
________________

•  …
restrictions

Optimized
Yield

So
how
to
get
all
of
these
factors
combined?


iGreen
profits
from
recent
technological
advances
that,
in
the
intended

combination,
will
strongly
influence
agricultural
progresses

Soil
Quality
Estimation

¨  Public
information
as
well
as
product
and
expert
knowledge

are
increasingly
digitized
and
available
via
the
internet,
such

as
different
kinds
of
maps
or
weather
data

Methods

Thematical
Maps

¨  Accessing
the
internet
is
possible
from

almost
all
locations
via
mobile
devices

¨  GPS
devices
for
positioning
are
easy
to

buy
or
are
even
part
of
a
mobile
phone

¨  Agricultural
engineering
provides
more
and
more
software

interfaces
for
the
automatic
situation-‐adaptive
control
(on-‐board

terminals,
sensor
technology,
…

So
we
are
going
to
establish
a
Service
and
Knowledge
Network


The
iGreen
Planning
System
combines
knowledge
subjects
and
information

objects
for
a
participative
public-‐private
knowledge
management

Agro
Databases

Geo
Catalogue

Data
Provision

Competence

Center

Planning

System

Global
Optimization
Public

Local
Optimization
Private

Free
Service

for
Farmers

Feedback

Feedback
Prognoses

Farmers
Location-‐based

Decision
Assistance


Flexible
Data
Management
is
a
challenge

“

Before you start talking about sensor data,
please provide me first an option to file, manage
and employ the data
“
Farmer
and
Contractor
Marx

¨  The
only
condition
is
individual
data
ownership

-‐
local
ﬁling
combined
with
controlled
interchange
within
the
iGreen
network


Public-‐private

knowledge
management


Knowledge
Management
is
a
process
for
improving
organizational
capabilities
by

better
use
of
individual
and
collective
knowledge
resources

1. 
Knowledge
Goals:
Define
all
capabilities
an
Definition

organization
should
build
on

2. 
Knowledge
Identification:
Identify
internal
and
Identify
Acquire

knowledge

external
knowledge
of
the
organization
knowledge

3. 
Knowledge
Acquisition:
Critical
capabilities
must
be
Define
knowledge

2
3

bought
or
otherwise
obtained

4. 
Knowledge
Development:
Produce
new
internal

Goals
1

wledge!
and
external
knowledge
(individual
&
collective
level)
Develop

5. 
Knowledge
Dissemination:
Define
who
should

Kno
Feedback
knowledge

know
what
and
at
what
level
of
detail,
and
how
the

organization
can
support
this
distribution
process
knowledge
Control 8

Mana gement! 4

6. 
Knowledge
Utilization:
Productive
deployment
of

organizational
knowledge
in
the
business/production
Preserve

Cycle!
7

process
of
the
organization
knowledge

7. 
Knowledge
Preservation:
Identify
valuable
5

knowledge,
store
it,
and
regularly
integrate
it
into
Use 6

Distribute

the
organizational
knowledge
base

knowledge
knowledge

8. Knowledge
Controlling:
Compare
initial
knowledge

goals
with
results
of
organizational
knowledge
magt.


Source:
G.J.B.
Probst,
S.
Raub,
and
K.
Romhardt.
Wissen
managen.
Gabler,
1997.
ISBN
3409193170.

In
terms
of
knowledge
management,
both
experts
and
farmers
have
their
own

knowledge
management
cycle

Decision
Support
System
(DSS)

Disseminate

Identify
Acquire

Use
knowledge
knowledge

knowledge

5
6
knowledge
2
3

Deﬁne

Preserve
knowledge

7
knowledge
Goals
1

Increase
Yields
Develop

Feedback
knowledge

Control

8
knowledge
Control
8
4

4
knowledge

Feedback

Develop
Deﬁne

knowledge

1
knowledge
Preserve
7

Goals
knowledge

Develop
DSS

3
5

Acquire
2
Use 6

Distribute

knowledge
Identify
knowledge
knowledge

knowledge


In
terms
of
knowledge
management,
both
experts
and
farmers
have
their
own

knowledge
management
cycle

Decision
Support
System
(DSS)

Disseminate

Identify
Acquire

Use
knowledge
knowledge

knowledge

5
6
knowledge
2
3

Deﬁne

knowledge

7
Preserve
Goals
1

knowledge

Increase
Yields
Develop

Feedback
knowledge

Control

8
knowledge
Control 8

4

4
knowledge

Feedback

Develop
Deﬁne

knowledge

1
knowledge
Preserve
7

Goals
knowledge

Develop
DSS

3
5

Acquire
2
Use 6

Distribute

knowledge
Identify
knowledge
knowledge

knowledge


In
terms
of
knowledge
management,
both
experts
and
farmers
have
their
own

knowledge
management
cycle

Decision
Support
System
(DSS)

Disseminate

Acquire

knowledge
knowledge

5
3

Deﬁne

knowledge

Goals
1

Increase
Yields
Develop

knowledge

4

4

Develop
Deﬁne

knowledge

1
knowledge

Goals

Develop
DSS

3

Acquire
Use 6

knowledge
knowledge


In
terms
of
knowledge
management,
both
experts
and
farmers
have
their
own

knowledge
management
cycle

Decision
Support
System
(DSS)
No
better

decision
support
!

Disseminate

Acquire

knowledge
knowledge

5
3

Define

knowledge

Goals
1

Increase
Yields
Develop

Not
Possible!
knowledge

4

4

Develop
Define

knowledge

1
knowledge

No
better
Goals

advice

Soil Type Develop
DSS

3
S

Acquire
Use 6

SI
IS
SL
sL

knowledge

L
LT
T
Mo
knowledge

No
better

map
material!
Soil
quality
map
from

the
1950’s

(Low
definition
and
outdated)


Available
agricultural
technology
allows
farmers
to
derive
accurate
and
up
to

date
soil
quality
maps

GPS
Technology
combined
with
agricultural
sensors
to
measure
the
yield

Moisture
Sensor

Mass
Flow
Sensor

GPS
Receiver

Task
Computer

User
Interface

Soil
quality
map
from
the
Soil
quality
map
from

1950’s
:
precision
ag.:

•  Outdated
•  Up
to
date

Source:
R.
Grisso,
M.
Alley,
and
P.
McCellan.
Precision
farming
tools:

yield
monitor.
Precision
Farming,
pages
442–502,
2003.

•  Low
resolution
•  High
resolution


A
collaborative
knowledge
management
approach
can
lead
to
better
agricultural

decision
support

Better
Decision

Support

Win-‐Win
Acquire

Disseminate

knowledge

5

Situation
knowledge

Define
3

knowledge

Goals
1

Increase
Yields
Develop

Possible
knowledge

4

4

Develop
Define

knowledge

1
knowledge

Better
DSS
Goals

Yield in
t/ha
Develop
DSS

3

0
-‐2
2,01
– 4

Use 6

4.01
– 6

Acquire
6.01
– 8
>8

knowledge
knowledge

Yield
maps

Better

(High
definition
&
up-‐to-‐date)

map
material!


Farmers
can
collaboratively
contribute
to
the
acquisition
of
better
geo-‐data
and

get
in
return
better
decision
support

Partial
collaboration:
Full
collaboration:

Data
is
only
shared
with
the
experts
Data
is
shared
within
the
community

Geo-‐Data

Information
Supply


Scenario
planning
allows

crop
forecast


Examples
of
Applications
and
Evaluations

The
collaborative
contribution
was
implemented
in
applications
for
biomass
and

logistics
planning

Biomass
Planning

Goal
• Computing
the
optimal
biomass
yield

based
on:

–  list
of
fields

–  production
plan

–  soil
quality

–  weather
conditions

Resources
• Experts
provide
models
and
weather
data

• Farmers
provide
accurate
soil
quality

maps
using
precision
agriculture
and
a

production
plan

Approach
• Soils
quality
maps
provided
by
a
farmer

are
used
to
improve
results
but
remain

confidential

• Models
are
improved
and
benefit

the
whole
community

Partial
collaboration!


Tasks
and
their
dependencies
of
Biomass-‐Yield-‐Models
(BYM)
are
transformed

into
a
scientific
workflow

Computational
steps
for
scientific
simulations
or
data-‐analysis
steps

n  Advantages
of
scientific
workflows:

•  Data
flow
approach
based
on
visual
programming

•  Modularity
and
reusability

•  Provenance
information
to
better
interpret
results
and

debug
errors

Source:
B.
Ludäscher,
I.
Altintas,
C.
Berkley,
D.
Higgins,
E.
Jaeger,
M.
Jones,
E.A.
Lee,
J.
Tao,
and
Y.
Zhao.
Scientific
workflow
management
and
the
Kepler
system:
Research
articles.

Concurr.
Comput.
:
Pract.
Exper.,
18:1039-‐1065,
August
2006.
ISSN
1532-‐0626.
doi:
10.1002/cpe.v18:10.

Based
on
various
public
sources
we
have
developed
a
Crop
Forecast
System

aiming
at
supporting
the
farmer
in
making
decisions

Pre-‐given
Goal

Rain
Result
Map
Crop
Forecast

Region
Intermediate

Result
BYM

Result
Table

Filter

Soil
Quality

Result
Chart

Agricltural
Crop
Land
Online

Rheinland-‐Pfalz
(FLOrlp)

Biomass

Yield

Models

Soil
Quality
Data
(BYM)

*
Zentralstelle
der
Länder
für
EDV-‐gestützte
Entscheidungshilfen

und
Programme
im
Pﬂanzenschutz


A
Mashup
of
maps
and
tabular
information
provides
a
intuitive
platform
for

biomass
planning

Excel
and
Google
Earth
export

Ecological
or
conventional
yield

3
rainfall
scenarios
(dry,
normal,
wet)

Results
for
ﬁelds

Results
for
subﬁelds


In
Google
Earth
subﬁeld-‐related
information
may
be
accessed
via
any
mobile

device


Exporting
the
information
to
an
Excel
spreadsheet
(with
Macros)
supports

realizing
a
production
plan


© andreas.dengel@dfki.de -

We
have
tested
the
forecast
on
a
farm
by
evaluating
four
tours*

Biomass
(t)

-‐15%
One
field
not
be

harvested

1.215 Weed
infestations

-‐2%

-‐10%
-‐19%

667 651
512 550
459 443

Tour
1
Tour
2
Tour
3
Tour
4

Forecast
Real

*
16
fields
to
be
harvested;
avg.
dist.
betw.
field
and
silo:
9.5
km;
average
exploitable
acreage:
2.44
ha


Logistic
planning
allows

to
remarkably
save
fuel


Examples
of
Applications
and
Evaluations

The
collaborative
contribution
was
implemented
in
applications
for
biomass
and

logistics
planning

Biomass
Planning

Logistics
Planning

Goal
• Computing
the
optimal
biomass
yield
• Computing

the
optimal
logistics
plan,

based
on:
i.e.,
route
and
costs

for:

–  list
of
fields
–  harvester(s)

–  production
plan
–  tractor(s)
with
trailers

–  soil
quality

–  weather
conditions

Resources
• Experts
provide
models
and
weather
data
• Experts
provide
models

• Farmers
provide
accurate
soil
quality
• Farmers
provide
accurate
GPS-‐Tracks
and

maps
using
precision
agriculture
and
a
meta-‐information
about
their
routes
using

production
plan
precision
agriculture
and
number
of

harvesters
and
tractors
available

Approach
• Soils
quality
maps
provided
by
a
farmer
• GPS-‐Tracks
and
meta-‐information

are
used
to
improve
results
but
remain
provided
by
a
farmer
are
sanitized
and

confidential
shared
with
the
whole
community

• Models
are
improved
and
benefit

• Models
are
improved
and
benefit

the
whole
community
the
whole
community

Full
collaboration!


Current
available
route
guidance
systems
do
not
provide
information
about

road
narrowing
nor
bridge
weights
limits


This
way,
we
also
developed
a
ﬁrst
collaborative
open-‐source
routing
system

for
utilities
vehicles
(CRUV)

Vehicle
type
(car,
lorry,
tractor)

-‐ Road
types:
Motorway,
highway,

country
road,
ﬁeld
path
with
priorities

(preferred,
normal,
avoid,
forbidden)

-‐ Tunnels
and
bridges
with
priorities

Vehicle
length,
weight,
height,
width

Rules
based
on
user
meta-‐information

stored
in
Open
Street
Maps


Compared
to
Map24,
there
is
no
diﬀerence
in
quality
in
traditional
route

planning

CRUV:
186
km
Map24:
186
km


However,
CRUV
reveals
its
advantages
when
including
the
additional

information,
e.g.
with
bridges
and
maximal
weight
allowed

Google
Maps
does
not

support
the
kind
of
query!

GoogleMaps:
1,5
km
CRUV:
2,8
km


A
logistic
planer

computes
transportation
costs
based
on
the
results
from
the

CRUV
and
biomass
planner

Overview
with
Biomass
and
costs

Field
repartition
with
respect
to
the
driving
distance

Max.
driving
distance
d

between
ﬁeld
and
POI

List
of
Fields
(<
d):
Point
of
interest
(ex:

-‐ Size
Biogas
plant)

-‐ Driving
distance

-‐ Crop

-‐ Yield

-‐ Transportation
costs

Slope
(of
the
road)

Route
between
POI
based
on
driving
distance


We
have
tested
the
forecast
on
a
farm
by
evaluating
four
tours*

Biomass
(t)

Transportation
costs
(EUR)

+4%

-‐15%
One
field
not
be
Higher
fuel

harvested
consumption

14.077
due
to
dry
soil

1.215 Weed
infestations

+57%

-‐3%
+8%

-‐2%

8.003
-‐10%
-‐19%
6.714 6.542 6.816
667 651 6.320
512 550 5.086
459 443

Tour
1
Tour
2
Tour
3
Tour
4
Tour
1
Tour
2
Tour
3
Tour
4

Forecast
Real
Forecast
Real

*
16
fields
to
be
harvested;
avg.
dist.
betw.
field
and
silo:
9.5
km;
average
exploitable
acreage:
2.44
ha


The
goals
of
iGreen
are
very
relevant
for
many
social
systems

¨  iGreen
follows
an
approach
leading
to
more
accurate
and
geographically
diﬀerentiated

agricultural
forecast
by

-‐  leveraging
existing
farmers
GPS-‐based
precision
agriculture
technology

-‐  collaboratively
acquiring
accurate,
high-‐resolution,
and
up-‐to-‐dare
geo-‐data

-‐  allowing
public
agricultural
institutions,
in
return,
to
use
this
geo-‐data
to
provide
farmers

with
better
decision
support
tools

¨  Biomass
Planner

-‐  A
decision
support
system
relying
on
this
approach
to
provide
better
biomass
planning

-‐  Both
the
biomass
model
and
soil
quality
maps
are
improved
but
no
data
is
shared
with

the
community.
Only
the
improved
model
beneﬁts
the
community

¨  Logistic
Planner

-‐  A
decision
support
system
relying
on
this
approach
to

provide
better
logistics
planning

-‐  Both
the
model
and
the
geo-‐data
are
shared
with
the

community


e the!
to clos
re going agement!
So, we a ge Man
d
Knowle

Cycle!
Moreover,

existing
soil
quality
maps

may
be
continuously
improved


Remember
that
forecast
is
based
on
the
data
coming
from
existing
soil
quality

maps!


Digital
soil
quality
maps
provided
by
state
government
allow
farmers
to
get
an

overview
about
the
quality
of
their
ﬁelds

40
Additional

information
may
be

accessed
via
the
lot

No.

33
44

61

53


Agricultural
engineering
provides
relevant
location-‐based
data

Humidity

Sensor

Recording
of
location,
crop,
humidity,

and
fuel
consumption:

Melt
Flow

Sensor


Location-‐based
recording
of
crop
data
leads
to
an
up-‐date
of
soil
quality
maps

Recorded
on
29-‐09-‐2008
Average
Value
12,23
t/ha

Try
Solids
100,40
t
Humidity
73,55
%

CROP
(t/ha)

from
to

HISTOGRAM


Decision
support
in
crop
production
provides
important
contributions
for

increasing
eﬃciency,
saving
resources,
and
reducing
environmental
impact

Exemplary
result
(10
farmers
in
the
north
of
the
German
state
Rhineland-‐Palatinate):

¨  Winter
wheat
is
the
most
important
crop
in
Germany
having
a
total

acreage
of
3
Mio
hectares.
Experts
estimate
that
each
hectare
of

winter
wheat
requires
about
200
kg
of
nitrate
per
year.
Nitrate

production
causes
about
50%
of
energy
consumption
in
crop

production

¨  Using
location-‐adaptive
fertilizer
dispersion
for
wheat
production

based
on
improved
soil
quality
maps
,
it
was
possible
to
save
ca.
5%

of
nitrates
without
reducing
the
amount
of
crop

¨  The
German
agriculture
may
…

...
lead
to
possible
energy
saving
potential
of
30.000
tons
of
nitrate
per
year

...
avoid
climate-‐relevant
emissions
of
257,000
tons
of
CO2
annually

...
reduces
the
amount
of
nitrate-‐containing
nutriments
in
bodies
of
water


The
goals
of
iGreen
are
very
relevant
for
many
social
systems

¨  Food
production
is
not
a
task
just
of
agriculture,
but
a
central
aim
of
the
global
society

¨  In
case
of
crisis,
food
security
has
to
be
guaranteed
by
both,
federal
and
private

organizations
(this
is
part
of
the
food
precaution
law
and
food
ensuring
law
in
Germany)

¨  iGreen
use
cases
and
demonstrators
prove
the
new
options
based
on
real
application

scenarios

¨  iGreen
SDK
provides
general
and
fundamental
components
as
open
source

¨  iGreen
documents
(processes,
scenarios,
interfaces)

provide
excellent
guidelines,
e.g.
who
communicates
with

whom
,why,
and
with
what
tools

Long-‐term
Public-‐Private
Partnerships
are
of

major
importance


Thank
you,
questions
are
welcome!


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