by Irmantas Čepulis | AgriFood Lithuania DIH
The STELLA Project aims to tackle pest challenges head-on with innovative monitoring technologies to safeguard crops, reduce economic losses, and support sustainable farming in Lithuania. As climate shifts bring new pest species and diseases, STELLA implements advanced solutions to effectively monitor and combat these threats.
Key Achievements from Year One:
Technology Implementation:
- 12 potato fields (2–19 ha) in the Marijampolė region were selected for satellite monitoring.
- 4 fields were equipped with connected insect traps and 2 fields included integrated weather stations.
Pest Focus:
- Early detection and prevention are critical to mitigating the impact of the devastating Potato Leafroll Virus (PLRV), which is transmitted by aphids and can cause up to 50% yield loss.
Testing and Simulation:
- Initial installations evaluated the technical feasibility of pest traps and data collection tools. This groundwork ensures a seamless transition to large-scale operations in subsequent years.
What’s Next in 2025?
New Field Locations: Adapted to crop rotation practices, 12 new fields will be monitored, with 4 equipped for intensive surveillance.
Enhanced Scouting Tools: Introducing the Eden Viewer Scouting System, leveraging AI-powered proximal sensing for detailed pest analysis from multiple perspectives.
Holistic Digital Platform: Developing a system for early pest detection and response, integrating satellite imagery, traps, and AI-driven analytics.
Why It Matters:
With climate change accelerating the migration of pests from Central Europe to Lithuania, innovative surveillance and containment are critical to safeguarding crops and ensuring sustainable agriculture. The STELLA Project provides farmers with advanced tools to protect their yields and support resilient farming practices
Join the Movement:
STELLA is building a network of farmers, advisors, and policymakers. Crowdsourced data collection through smartphone scouting apps enhances pest monitoring efforts and is supported by AI-driven models for accurate symptom classification and predictive analysis.
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