Machine Learning and Complex Event Processing: Shaping the Future of Smart Agriculture

The incorporation of advanced technologies in Smart Agriculture has opened new possibilities for enhancing productivity and sustainability. Among these, Machine Learning (ML) has been widely adopted for crop recommendation and yield prediction by analyzing historical agricultural data, achieving impressive prediction accuracies with various algorithms. However, most existing systems are static and do not adapt to real-time changes in environmental conditions. This paper surveys recent advancements in ML-based crop recommendation systems and explores the emerging role of Complex Event Processing (CEP) in enabling real-time decision-making in Smart Agriculture. CEP engines can analyze continuous streams of sensor data to detect critical patterns such as drought, nutrient stress, triggering alerts or adjustments to crop choices. We discuss how the fusion of ML and CEP offers a dynamic and intelligent decision support system (DSS) for farmers, enabling context-aware recommendations that respond to real-time field conditions. This survey identifies existing gaps in integrating streaming data with contextual decision-making in agriculture and highlights the potential of combining CEP and ML as a promising direction for future research.