Early Pest Detection in Greenhouses

1. Vincent Martin, Sabine Moisan INRIA Sophia Antipolis Méditerranée, Pulsar project-team, France Early Pest Detection in Greenhouses

2. Motivation: reduce pesticide use • Agricultural issues: • 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. • Reduce time overhead of workers in charge of greenhouse biological monitoring • Understand better pest population behaviors • Computer vision issues: • Automatically identify and count populations to allow rapid decisions • Improve and cumulate knowledge of greenhouse attack history

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

4. Proposed Approach Intelligent Acquisition Detection Classification Tracking Behaviour Recognition • Automatic vision system for in situ,non invasive, and early detection • Based on a video sensor network • Lined up with cognitive vision research (machine learning, a priori knowledge…) Image sequences with moving objects Pest counting results Regions of interest Pest identification Scenarios (laying, predation…) Pest trajectories Current work Future work

5. First DIViNe Prototype 400€ • 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.

6. Intelligent Acquisition Module • Scheduled image sequence acquisition: • at specific time intervals, • on motion detection • Distant tuning of each sensor settings (resolution, frame rate) • Storage and retrieval of relevant video data

7. Detection Module • Handle illumination changes • due to sun rotation, shadows, reflection… • Adapt algorithms to deal with different image contexts video clip Sunny context with shadows and high contrast Cloudy context with reflections and low contrast

8. Detection Module: Preliminary Results • Weakly supervised learning to acquire context knowledge from global image characteristics • Context identification for background model selection video clip

9. Classification Module: Preliminary Results

10. Conclusion and Future Work • A greenhouse equipped with a video camera network • 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/

11. Behavior Recognition ModuleLaying scenario example • Behavior description based on a generic declarative language relying on a video eventontology • Scenario models based on the concepts of states and events related to interesting objects. • state = spatiotemporal property valid at a given instant and stable on a time interval. • event = meaningful change of state. • scenario = combination of primitive states and events by using logical, spatial or temporal constraints between objects, events, and states. • 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); • }

12. Plant Organs Monitoring • Issues: • Plant motion estimation ( + need of auto focus sensors) • Non planar field of view • choice of the sensor positions