Current methods of insect and pest monitoring are inefficient. They typically require installation and continual manual monitoring of pheromone traps. The visual identification and counting of parasites insects must be done by a field operator who, based on the identification, must decide whether to intervene.
Our goal is to limit human intervention in the field to a minimum, reducing the costs of monitoring thanks to the sending of real-time information on the presence of parasites.
This information, combined with other data gathered in the field, are processed by self-adaptive algorithms that can anticipate and therefore prevent attacks, limiting crop loss for farmers.