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1 Towards a Video Camera Network for Early Pest Detection in Greenhouses Vincent Martin 1, Sabine Moisan 1 Bruno Paris 2, Olivier Nicolas 2 1. I N R I.

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Presentation on theme: "1 Towards a Video Camera Network for Early Pest Detection in Greenhouses Vincent Martin 1, Sabine Moisan 1 Bruno Paris 2, Olivier Nicolas 2 1. I N R I."— Presentation transcript:

1 1 Towards a Video Camera Network for Early Pest Detection in Greenhouses Vincent Martin 1, Sabine Moisan 1 Bruno Paris 2, Olivier Nicolas 2 1. I N R I A Sophia Antipolis Méditerranée, Pulsar project-team, France 2.CREAT, Chambre d'Agriculture des Alpes Maritimes, France

2 2 2/8 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

3 3 3/8 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) Aphid photo: Inra (Brun)

4 4 4/8 DIViNe 1 : A Decision Support System 1 Detection of Insects by a Video Network Identification and counting of pests Manual methodDIViNe system Result delivery Up to 2 daysNear 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

5 5 5/8 First DIViNe Prototype Network of 5 wireless video cameras (protected against water projection and direct sun). In a 130 m 2 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

6 6 6/8 Processing Chain Intelligent Acquisition Intelligent Acquisition Detection Classification Tracking Behaviour Recognition Behaviour Recognition Regions of interest Pest identification Pest trajectories Scenarios (laying, predation…) Image sequences with moving objects Pest counting results Current work Future work

7 7 7/8 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

8 8 8/8 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

9 9 9/8 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); }

10 10 10/8 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


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