xFarm Technologies has received 1 million euros from the European Innovation Council’s EIC Accelerator to develop xTrap, a smart insect trap that monitors trends in the capture of pest insects and provides timely insights into pesticide use.
The smart insect trap called xTrap is equipped with a high-resolution camera capable of monitoring insect species in a given crop field. xTrap’s innovation is based on the Machine Learning algorithm developed by xFarm Technologies, which automatically recognises and counts insects, and on a predictive model that forecasts trends in infestations of pest insects. xTrap autonomously sends the data it collects to the xFarm platform.
This tool allows farmers to check catch trends on a daily basis from both smartphone and computer. It also allows them to receive advice on the timing of phytosanitary treatments to implement, reducing waste, costs and increasing environmental sustainability.
Three versions of the insect trap are currently available: Delta, which uses pheromones; Stink, which allows for counting the passage of insects such as the brown marmorated stink bug; and Color, which employs color sheets.
The R&D division of xFarm Technologies does not rule out bringing other models of the insect trap to the market in the future, although the funds granted by the European Innovation Council will be used mainly to implement and upgrade the technology at the heart of the trap, i.e., to improve the algorithm and prediction model.
“xTrap can be a valuable ally for those involved in monitoring, particularly in high-value-added crops, such as grapevines and fruit crops, which are often subject to pest attack. This tool is designed to support the work of technicians at farms, wineries, trade associations, cooperatives and research institutions, who increasingly need comprehensive and 4.0 tools for insect monitoring and control,” said Martino Poretti, Head of IoT and xTrap project manager.
Acoording to Poretti, the main objective that xFarm wants to achieve with this funding is to enhance the functions of the algorithm and of the predictive models underlying the insect trap, and to provide a technology capable of saving time and optimising applications, ensuring savings and greater sustainability.