Predicting Corn Infections Using Weather Data and Machine Learning
Year: 2025
Type: article
Abstract: Effective pest management is essential for protecting crop yields and ensuring food safety. Traditional pesticide-based approaches often compromise environmental and human health. AGRIVI’s AI Pest Detection Algorithm presents a sustainable alternative by integrating pest appearance data, weather forecasts, and geolocation to generate timely, targeted alerts. This enables farmers to act proactively, reduce pesticide dependency, and enhance food safety. Within the EFRA Project, AGRIVI collaborates with Wageningen Food Safety Research and the Consiglio Nazionale delle Ricerche to refine the algorithm in response to climate-induced changes in pest behavior. Revised criteria and real-world validation aim to improve the system's accuracy and lead time, equipping farmers with early alerts to apply the most effective pest control measures and minimize yield losses.
Authors: Rumeau, Morgane, Folnovic, Tanja
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
Funder: European Commission
Award: Extreme Food Risk Analytics
OpenAlex ID: W6949540557
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