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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. Towards a Video Camera Network for Early Pest Detection in Greenhouses. Motivations.

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Towards a Video Camera Network for Early Pest Detection in Greenhouses

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  1. Vincent Martin1, Sabine Moisan1 Bruno Paris2, Olivier Nicolas2 1. I N R IA Sophia Antipolis Méditerranée, Pulsar project-team, France 2.CREAT, Chambre d'Agriculture des Alpes Maritimes, France Towards a Video Camera Network for Early Pest Detection in Greenhouses

  2. 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. 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. DIViNe1: A Decision Support System1Detection of Insects by a Video Network

  5. 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

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

  7. Preliminary Results video clip

  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 http://www-sop.inria.fr/pulsar/projects/bioserre/

  9. 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. 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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