This Slovenian AI startup is empowering farmers with advanced insights into pests

By Vignesh R.

In the ever-evolving world of agriculture, effective pest management plays a critical role in ensuring optimal crop health and maximising yields.

Traditionally, farmers and agronomists relied on manual monitoring techniques and reactive pest control measures, leading to inefficiencies and suboptimal results.

In recent years, the advent of advanced technologies have revolutionised farmers’ methods to monitor and manage pests.

Meet Trapview!

Image credits: TrapView LinkedIn

Developed by the Slovenian company EFOS, Trapview is an AI-powered platform for monitoring and forecasting pest and insect populations.

Talking about TrapView, Matej Stefancic, CEO and co-founder of EFOS, says, ″Our company’s mission has always been to strive for a clean environment and safe food, professionally and privately.″

″Initially, the business focused on animal tracking and identification through veterinary information systems. During one of the larger projects, an inquiry about monitoring an insect pest was raised by the customer. Having an idea that was aligned with our strategy and values led us to develop Trapview,″ he explains.

What exactly does TrapView do?

Trapview is a novel crop protection decision support solution providing near-real-time automated monitoring and accurate forecasts about pest insect development.

Stefancic says forecasts are based on data collected by automated traps coupled with AI-enhanced machine vision and machine learning processing that allows much greater accuracy of information about pest populations than ever before.

The company’s automated traps send real-time data to farmers about pest situations in their fields and use artificial intelligence to analyse and predict pest dynamics.

Farmers can then make informed and timely crop protection decisions in a targeted manner instead of spraying entire crops with pesticides preventively.

TrapView’s AI processing tech

Stefancic says AI has a twofold responsibility within TrapView’s data processing framework.

Primarily, it leverages its machine vision capabilities to analyse images captured by pest insect traps, effectively detecting and marking target pests.

The quantity of markings serves as an indicator of the pest population, and these pest statistics are one of the critical input data for AI’s second role: machine learning.

″At the machine learning step, multiple data streams key to understanding pest development are fed to the algorithms and then the system processes. This provides an accurate prediction of the trends of the adult pest population as well as pest development stages,″ he tells Silicon Canals.

Continue reading at Silicon Canals.