Section: Application Domains
Detection and Behavior Recognition of Bioagressors
In the environmental domain, Pulsar is interested in the automation of early detection of bioagressor, especially in greenhouse crops, in order to reduce pesticide use. Attacks (from insects or fungi) imply almost immediate decision-taking to prevent irreversible proliferation. The goal of this work is to define innovative decision support methods for in situ early pest detection based on video analysis and scene interpretation from multi camera data. We promote a non-destructive and non-invasive approach to allow rapid remedial decisions from producers. The major issue is to reach a sufficient level of robustness for a continuous surveillance.
During the last decade, most studies on video applications for biological organism surveillance were limited to constrained environments where camerawork conditions are controlled. By contrast, we aim at monitoring pests in their natural environment (greenhouses). We thus intend to automate pest detection, in the same way as the management of climate, fertilization and irrigation which are carried out by a control/command computer system. To this end, vision algorithms (segmentation, classification, tracking) must be adapted to cope with illumination changes, plant movements, or insect characteristics.
Traditional manual counting is tedious, time consuming and subjective. We have developed a generic approach based on a priori knowledge and adaptive methods for vision tasks. This approach can be applied to insect images in order, first, to automate identification and counting of bio-aggressors, and ultimately, to analyze insect behaviors. Our work takes place within the framework of cognitive vision  . We propose to combine image processing, neural learning, and a priori knowledge to design a system complete from video acquisition to behavior analysis. The ultimate goal of our system is to integrate a module for insect behavior analysis. Indeed, recognition of some characteristic behaviors is often closely related to epicenters of infestation. Coupled with an optimized spatial sampling of the video cameras, it can be of crucial help for rapid decision support.
Most of the studies on behavior analysis have concentrated on human beings. We intend to extend cognitive vision systems to monitor non-human activities. We will define scenario models based on the concepts of states and events related to interesting objects , to describe the scenarios relative to white insect behaviors. We shall also rely on ontologies (such as a a video event one). Finally, in the long term, we want to investigate data mining for biological research. Indeed, biologists require new knowledge to analyze bioagressor behaviors. A key step will to be able to match numerical features (based on trajectories and density distributions for instance) and their biological interpretations (e.g., predation or center of infestation).
This work takes place in a two year collaboration (ARC BioSERRE) between Pulsar (INRIA Sophia Antipolis - Méditerranée), Vista (INRIA Rennes - Bretagne Atlantique), INRA Avignon UR407 Pathologie Végétale (Institut National de Recherche Agronomique), CREAT Research Center (Chambre d'Agriculture des Alpes Maritimes) started in 2008.