ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF WATER SCARCITY IN AGRICULTURE

Water scarcity has emerged as one of the most critical challenges affecting global agricultural productivity, particularly in arid and semi-arid regions. The integration of Artificial Intelligence (AI) into water resource management offers a promising approach to predict, monitor, and mitigate water shortages. This study explores the application of AI techniques, including machine learning and deep learning models, in forecasting agricultural water scarcity. By analyzing historical climate data, soil moisture levels, crop water requirements, and satellite imagery, AI systems can provide accurate and timely predictions. The research highlights how predictive analytics improves irrigation efficiency, reduces water wastage, and supports sustainable agriculture. The findings demonstrate that AI-driven models significantly outperform traditional statistical approaches in accuracy and adaptability.