Download Insects - Inria Sophia Antipolis

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Towards a Video Camera Network for
Early Pest Detection in Greenhouses
Vincent Martin1, Sabine Moisan1
Bruno Paris2, Olivier Nicolas2
1. I N R I A Sophia Antipolis Méditerranée, Pulsar project-team, France
2.CREAT, Chambre d'Agriculture des Alpes Maritimes, France
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Motivations
• Temperature and hygrometric conditions inside a greenhouse favor
frequent and rapid attacks of bioagressors (insects, spider mites, fungi).
• Difficult to know starting time and location of such attacks.
• Need to automatically identify and count populations to allow rapid
decisions
• Help workers in charge of greenhouse biological monitoring
• Improve and cumulate knowledge of greenhouse attack history
• Control populations after beneficial releases or chemical applications
Collaborative Research Initiative BioSerre between INRIA, INRA,
and Chambre d’Agriculture des Alpes Maritimes
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Objectives
• Context: Integrated Pest Management
• Early pest detection to reduce pesticide
use
• Approach: Automatic vision system for in
situ, non invasive, and early detection
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based on a video sensor network
using video processing and
understanding, machine learning, and
a priori knowledge
Help producers to take protection
decisions
White fly
photo : Inra (Brun)
Aphid
photo: Inra (Brun)
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DIViNe1: A Decision Support System
1Detection
of Insects by a Video Network
Identification and counting of pests
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Manual method
DIViNe system
Result delivery
Up to 2 days
Near real-time
Advantages
Discrimination capacity
Autonomous system,
temporal sampling, cost
Disadvantages
Need of a specialized operator
(taxonomist); precision vs time
Predefined insect types;
video camera installation
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First DIViNe Prototype
• Network of 5 wireless video
cameras (protected against water
projection and direct sun).
• In a 130 m2 greenhouse at
CREAT planted with 3 varieties of
roses.
• Observing sticky traps
continuously during daylight.
• High image resolution
(1600x1200 pixels) at up to 10
frames per second.
• Automatic data acquisition
scheduled from distant
computers
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Processing Chain
Intelligent
Acquisition
Image sequences with
moving objects
Current work
Detection
Regions of interest
Classification
Pest identification
Pest counting results
Tracking
Pest trajectories
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Future work
Behaviour
Recognition
Scenarios (laying,
predation…)
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Preliminary Results
video clip
Acquisition: sticky trap
zoom
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Detection: regions of
interest in white by
background subraction
Classification: regions
labeled according to insect
types based on visual
features
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Conclusion and Future Work
• A greenhouse equipped with
video cameras
• A software prototype:
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Intelligent image acquisition
Pest detection (few species)
• Future:
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Detect more species
Observe directly on plant
organs (e.g. spider mites)
Behaviour recognition
Integrated biological sensor
See http://www-sop.inria.fr/pulsar/projects/bioserre/
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Laying scenario example
state: insideZone( Insect, Zone )
event: exitZone( Insect, Zone )
state: rotating( Insect )
scenario: WhiteflyPivoting( Insect whitefly, Zone z ) {
A: insideZone( whitefly, z ) // B: rotating( whitefly );
constraints: duration( A ) > duration( B );
}
scenario: EggAppearing( Insect whitefly, Insect egg, Zone z ) {
insideZone( whitefly, z ) then insideZone( egg, z );
}
main scenario: Laying( Insect whitefly, Insect egg, Zone z ) {
WhiteflyPivoting( whitefly, z ) //
loop EggAppearing( egg, z ) until
exitZone( whitefly, z );
then send(”Whitefly is laying in ” + z.name);
}
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Add on
• Expert knowledge of white flies: choose features for detection and
classification
• An ontology for the description of visual appearance of objects in
images based on:
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Pixel colours
Region texture
Geometry (shape, size,…)
• Adaptive techniques to deal with illumination changes, moving
background by means of machine learning
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