AI-Driven pesticide risk prediction models and multilingual learning interfaces to increase accessibility

Abstract

The use of Artificial Intelligence (AI) in agriculture has significantly enhanced pesticide risk assessment and decision-making. AI-driven pesticide risk prediction models provide accurate forecasting, minimizing environmental hazards and ensuring food safety. However, accessibility to such innovations remains a challenge, particularly in multilingual societies. This study explores AI-based pesticide risk models and the role of multilingual learning interfaces in democratizing access to agricultural insights. A hybrid framework integrating machine learning models for risk assessment and Natural Language Processing (NLP)-powered interfaces is proposed. The study highlights how AI can bridge linguistic barriers, improve risk mitigation strategies, and ensure broader adoption among farmers.