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, France
2.CREAT, Chambre d'Agriculture des Alpes Maritimes, France

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 Help producers to take protection decisions
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 System 1Detection 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 capacity
Autonomous system, temporal sampling, cost
Disadvantages
Need of a specialized operator (taxonomist); precision vs time
Predefined insect types; video camera installation
Manpower cost, cheap camera

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

7. Preliminary Results video clip Acquisition: sticky trap zoom
Detection: regions of interest in white by background subraction
Classification: regions labeled according to insect types based on visual features

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. 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