Abstract

Artificial Intelligence (AI) is revolutionizing the agricultural industry, offering tools to optimize crop yield predictions with high accuracy and efficiency. By leveraging machine learning (ML), deep learning (DL), remote sensing, and data analytics, farmers can make informed decisions that enhance productivity and resource use. This paper explores the integration of AI techniques in crop yield prediction, evaluates current methodologies, and proposes a hybrid model combining remote sensing data with real-time sensor inputs to enhance predictive accuracy.