Enhanced Predictive Capabilities for Pest Alarms
Key Objective
To build an algorithm to boost prediction accuracy and provide optimized recommendations for pest management
Pests pose a significant threat to crop health, leading to substantial losses in agricultural productivity. Timely and accurate pest detection is crucial for effective pest management. AGRIVI’s existing pest and disease algorithm utilizes pest appearance criteria, weather forecast data, and location data to provide timely alerts to farmers about potential pest invasions. The algorithm predicts the type of pest, the location, and the time of the potential invasions, aiding farmers in their protective measures.
The updated algorithm aims to enhance prediction accuracy and deliver optimized recommendations for pest management. This improvement is essential for minimizing crop damage and failure while ensuring sustainable agricultural practices. The updated system will undergo real-life tests with current AGRIVI users, encompassing a diverse range of crops and varying regional climates to ensure its effectiveness and reliability across different farming practices, climates, and crop types.
- Agro-climatological (AGRIVI & Public Data)
- Meteo- climatological (AGRIVI & Public Data)
- Irrigation planning (AGRIVI & Public Data)
- Primary feedback and interviews from farmers (Public Data)
- Corrective Actions from agronomists (Agrivi & Public Data)
- Incidents (e.g., recalls, border rejections) due to relevant primary produce ingredients (Public Data)
- Lab test results directly related to observed chemical residue levels on the targeted products (Public Data)
- Inspection results for relevant primary producers that highlight emerging food safety issues with critical points at the farm level (Public Data)
- Food safety news and expert opinion pieces on current and emerging risks (Public Data)
- Best practices for the specific countries (Public Data)
- Consumer reports and discussions (Public Data)
- Relevant scientific reports and publications (Public Data)
In this use-case we will focus on Enhanced Predictive Capabilities for Pest alarms, by initially focusing on 3 EU countries (Croatia, Romania, Bulgaria). We will deploy and train AI models using the EFRA privacy-preserving approach in 2 use-cases. Use-case 1: AI-Enhanced Pest Prediction Alerts and Use-case 2: Real-Time Farmer Feedback Loop. The first one is strategically crafted to significantly enhance our understanding of how pest behavior shifts in response to changing climate conditions. It is structured around two main phases: Data-Informed Rule Extraction and Predictive AI Model Development. The second one underscores the significance of leveraging feedback from farmers, who confirm the presence of pests through a pest alarm system. The on-field feedback not only facilitates the real-time detection of pests but also plays a crucial role in enhancing the AI model’s accuracy.
Accurate prediction requires deep data analysis and an understanding of various factors such as climate conditions, crop types, and pest behaviors. The updated algorithm aims to boost prediction accuracy and provide optimized recommendations for pest management in diverse circumstances.
Use Case Leader
Contact person
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Josipa Vrkljan
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Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission-EU. Neither the European Union nor the granting authority can be held responsible for them.
