Video Camera Network for Early Pest Video Camera Network for

Video Camera Network for Early
y Pest
Detection in Greenhouses
Réseau de capteurs in situ pour la détection précoce
d BIOagresseurs
des
O g
d
dans lles cultures
l
sous S
SERRE
INRIA Sophia Antipolis - Méditerranée,
Méditerranée PULSAR Team
INRIA R
Rennes - Bretagne
B t
Atl
Atlantique,
ti
VISTA T
Team
Ch
Chambre
b d'A
d'Agriculture
i lt
d
des Al
Alpes Maritimes,
M iti
CREAT R
Research
hC
Center
t
INRA Avignon, UR407 Pathologie Végétale
é é
Objectives
Research Issues
A t
ti real-time
l ti
id surveillance
ill
tto detect
d t t and
d track
t k bioagressors
bi g
Automatic
video
S
t early
l pestt detection
d t ti
t allow
ll
i t
t d pestt managementt methods
th d
Support
to
integrated
and
a
d reduce
educe pes
pesticide
c de use
use.
Develop
system
for in situ,, non invasive,, early
p a decision support
pp
y
y detection
based on a video sensor network,
network video analysis and cognitive vision.
vision
Motivations
•
In situ detection: implies introducing sensors in greenhouse.
greenhouse Currently
•
Cognitive
Cog
e vision
s o app
approach:
oac co
combine
e image
age processing,
p ocess g, machine
ac
e learning
ea
ga
and
d
a priori knowledge,
knowledge in a complete processing chain:
Temperature and hygrometric conditions inside a greenhouse favor frequent
and rapid attacks of bioagressor (insects,
(insects acarids,
acarids fungi).
fungi)
1. Acquisition:
Sampling
objects
on a large
p g strategy:
gy we have small,, complex
p
j
g
surface We need to define an optimal spatial sampling (INRIA &
surface.
CREAT)
Difficult to know starting time and location of such attacks.
Need
to
rapid
N d to
t identify
id tif and
d countt populations
l ti
t allow
ll
id decision
d i i
ttaking.
ki
Acarids
b
i
i k traps, iin the
h ffuture plant
l
i
sensors observing
sticky
organs, e.g. growing
stems for early detection of mature white flies.
Data flow processing : intelligent acquisition process to record
i
l when
h
ti
i detected.
d t t d
images
only
an iinsectt motion
is
Insects
2.
Detection:
context
by
, e.g. llearning
2 D
t ti
t t adaptation
d t ti
b learning
l
i techniques
t h i
i
the visual appearance
pp
of p
pests ((INRIA PULSAR).
)
Spider Mite
3. Classification: adaptive
p
algorithms
g
to cope
p with illumination changes
g
during daytime (INRIA PULSAR) + a priori knowledge of insects to
detect (CREAT & INRA)
INRA).
Aphid
Photo : Inra ((Brun))
Photo : Inra ((Brun))
4 Tracking: dynamic environment necessitates to accommodate plant
4.
movements
t (INRIA VISTA)
VISTA).
Fungi
Oïdium
5. Behavior recognition (egg
laying predation): use of
laying,
hi h level
l
l scenario
i
a high
description
desc
pt o language
a guage
(INRIA PULSAR).
PULSAR)
Thrips
Photo : Inra ((Boissard))
Photo : Inra ((Brun))
•
Botrytis
Female white flyy
laying its eggs
Preliminary
y results
White fly
Photo : Inra
Photo : Inra ((Brun))
Prototype
In Situ Acquisition Module
A network
video
cameras ((protected
t
k off 5 wireless
i l
id
t t d
against
g
water p
projection
j
and direct sun).
)
Acquisition:
sticky
zoom
A
i iti
ti k trap
t
In a 130 m2 greenhouse at CREAT planted with 3
i i off roses.
varieties
Classification:
region
Cl ifi ti
i llabeled
b l d
according to insect types
Expected
E
dR
Results
l
A complete (steps 1. to 5.) real time video surveillance system for in situ early
detection of pests in greenhouses (DIViNe).
(DIViNe)
Observing sticky traps continuously during
d li ht
daylight.
High image resolution (1600x1200 pixels) at up
to 10 frames per second
second.
Detection:
region
D t ti
i off iinterest
t
t in
i
white
A video camera on its
stand observing a
sticky trap
Video cameras positioned uniformly in the
plane
horizontal plane.
An extensible and reusable software architecture
architecture.
A cognitive vision approach (image processing and understanding,
understanding machine
lea ning and a priori
learning,
p io i knowledge)
kno ledge) to p
provide
o ide a robust
ob st and versatile
e satile s
system.
stem
In the long term: data mining for biological research to analyze bioagressor
b h i
behaviors.
Automatic data acquisition
q
scheduled from
distant computers
Publications
Towards a Video Camera Network for Early
y Pest Detection in Greenhouses,,
Vincent Martin
Martin, Sabine Moisan (INRIA
PULSAR), Bruno Paris,
(INRIA-PULSAR)
Paris Olivier Nicolas
(CREAT) ENDURE International Conference,
Conference La Grande Motte France Oct.
Oct
(CREAT),
2008.
E l Pest
P
D
i iin G
h
i and
dS
bi
M
i
ICPR
Early
Detection
Greenhouses
, Vi
Vincent M
Martin
Sabine
Moisan,
VAIB Workshop, Tampa USA, Dec. 2008
Funding from Provence-Alpes-Cote
d’Azur
d
Azur Région
Prototype
ototype dep
deployment
oy e t a
and
d wireless
e ess video
deo ca
camera
e a network.
et o
J
Journées
é nationales
ti
l ARC 2008
2008, Sophia
S hi Antipolis,
A ti li O
Octobre
t b 2008