Application of artificial intelligence and machine learning approaches for predicting farmers behaviour during agricultural drought

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in predicting farmers' behavior during agricultural drought has brought significant advancements in enhancing agricultural resilience and sustainability. Research indicates that ML algorithms can analyze diverse datasets on environmental conditions, crop health, and economic factors, empowering farmers to make informed decisions on resource management. AI shows promise in optimizing water conservation practices, particularly crucial in drought-prone regions, leading to improved productivity and ecological stewardship in agricultural supply chains. Ensuring culturally aware AI applications is emphasized for accessibility across diverse agricultural settings, especially in developing regions with traditional farming practices.