Enhanced Predictive Capabilities for Pest Alarms
Key Objective
To build an algorithm to boost prediction accuracy and provide optimized recommendations for pest management
Pests continue to pose a significant risk to agricultural productivity, particularly as climate variability affects pest behavior and distribution. Traditional pest management often relies on reactive chemical treatments, leading to overuse of pesticides, increased costs, and environmental harm. This use case addresses the need for early, accurate, and location-specific pest detection by improving AGRIVI’s AI-powered prediction algorithm. The goal is to enable farmers to take preventive and targeted crop protection measures, ultimately reducing pesticide use and supporting more sustainable agricultural practices.
To support accurate and adaptive pest prediction, the system draws on a combination of proprietary and field-based data:
- Private Data:
- Detailed meteorological and agro-climatic datasets
- AGRIVI’s internal pest management guidelines, including expert-defined pest appearance conditions and treatment recommendations
- Piloting Data (2024–2025):
- Verified pest occurrence records collected during field piloting activities
- Field validation results and qualitative feedback from participating farmers and agronomists, used to assess and refine algorithm performance
This use case focuses on enhancing predictive capabilities for pest alerts through the deployment and iterative training of AI models in three EU countries: Croatia, Romania, and Bulgaria. This Use case is piloted across two complementary scenarios:
- AI-Enhanced Pest Prediction Alerts – using weather and agronomic data to anticipate pest outbreaks
- Real-Time Farmer Feedback Loop – enabling farmers to confirm pest sightings and contribute directly to model refinement
The algorithm is designed around pest appearance criteria, including key environmental indicators such as temperature, humidity and rainfall. When current weather conditions match the profile of a known pest, the system automatically generates alerts to inform farmers of likely pest activity.
The model was first piloted in 2024 on corn crops and later enhanced in 2025 to include wheat and sunflower. These farms are distributed across diverse agro-climatic zones in the target countries.
- A highly accurate and scalable pest prediction algorithm capable of supporting different crops and climate zones.
- Reduced pesticide usage through earlier detection and more targeted application.
- A replicable model that EFRA users can access using their own field-level weather data.
- A validated feedback loop that makes pest detection more dynamic, responsive, and adaptive to changing conditions.
