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.

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
  • Josipa Vrkljan

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