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1
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, France2.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 Collaborative Research Initiative BioSerreBioSerre between INRIA, INRA, between INRIA, INRA, and Chambre d’Agriculture des Alpes Maritimesand 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
• 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)
Aphidphoto: Inra (Brun)
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DIViNe1: A Decision Support System1Detection of Insects by a Video Network
Identification and counting of pests
Manual method DIViNe system
Result delivery Up to 2 days Near real-time
Advantages Discrimination capacityAutonomous system, temporal sampling,
cost
DisadvantagesNeed 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
IntelligentAcquisition
IntelligentAcquisition
DetectionDetection
ClassificationClassification
TrackingTracking
BehaviourRecognition
BehaviourRecognition
Regions of interest
Pest identification
Pest trajectories Scenarios (laying, predation…)
Image sequences with moving objects
Pest counting results
Current work Future workCurrent work Future work
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Preliminary Results
Acquisition: sticky trap zoom
Detection: regions of interest in white by
background subraction
Classification: regions labeled according to insect
types based on visual features
video clip
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Conclusion and Future Work
• A greenhouse equipped with video cameras
• A software prototype:• Intelligent image acquisition
• Pest detection (few species)
• Future:• 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:
• Pixel colours
• Region texture
• Geometry (shape, size,…)
• Adaptive techniques to deal with illumination changes, moving background by means of machine learning